DOL-ILAB SDC - Section_10
Pablo Diego-Rosell, PhD
2021-10-15
- Setup filenames
- Setup data, functions and create dictionary for dataset review
- Direct PII: variables to be removed
- Direct PII-team: Encode field team names
- Small locations: Encode locations with pop <100,000 using random large numbers
- Indirect PII - Ordinal: Global recode or Top/bottom coding for extreme values
- Indirect PII - Categorical: Recode, encode, or Top/bottom coding for extreme values
- Matching and crosstabulations: Run automated PII check
- Open-ends: review responses for any sensitive information, redact as necessary
- GPS data: Displace
- Save processed data in Stata and SPSS format
rm(list=ls(all=t))Setup filenames
filename <- "Section_10" # !!!Update filename
functions_vers <- "functions_1.8.R" # !!!Update helper functions fileSetup data, functions and create dictionary for dataset review
source (functions_vers)Visually inspect variables in "dictionary.csv" and flag for risk, using the following flags:
# Direct PII: Respondent Names, Addresses, Identification Numbers, Phone Numbers
# Direct PII-team: Interviewer Names, other field team names
# Indirect PII-ordinal: Date of birth, Age, income, education, household composition.
# Indirect PII-categorical: Gender, education, ethnicity, nationality,
# occupation, employer, head of household, marital status
# GPS: Longitude, Latitude
# Small Location: Location (<100,000)
# Large Location (>100,000)
# Weight: weightVar
# Household ID: hhId,
# Open-ends: Review responses for any sensitive information, redact as necessary Direct PII: variables to be removed
# !!!No Direct PIIDirect PII-team: Encode field team names
# !!!No Direct PII-teamSmall locations: Encode locations with pop <100,000 using random large numbers
# !!!No Small LocationsIndirect PII - Ordinal: Global recode or Top/bottom coding for extreme values
# Focus on variables with a "Lowest Freq" in dictionary of 30 or less.
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q2)[na.exclude(mydata$eh_s10q2)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q2", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q2. Q711: In the last 7 days how much did the household spend on Bread and Cereals?
## -998 0 1 4 12 18 20 24 25 28 30 35 36 38 40 44 45 48 50 52 54 55 56 60 63 64 67 68 70 72 76 80
## 1 6 3 1 3 2 7 2 1 1 4 2 2 1 5 2 1 1 9 1 1 1 1 11 1 1 1 3 4 2 1 4
## 84 85 88 89 90 96 98 100 104 105 108 112 113 114 120 124 125 126 128 129 130 131 132 134 135 140 142 143 144 145 147 149
## 1 2 1 1 6 1 2 18 2 1 2 1 1 1 6 1 1 1 1 1 6 1 2 1 4 14 1 1 1 1 2 1
## 150 151 152 154 158 159 160 161 164 165 167 168 170 171 175 176 177 178 180 182 183 185 188 189 190 194 195 196 199 200 203 205
## 9 1 1 3 1 1 6 2 1 1 1 1 1 1 2 1 1 1 7 2 1 3 1 1 2 1 1 1 1 40 2 4
## 210 211 213 214 215 216 218 220 221 222 224 225 226 227 230 232 233 234 235 236 237 238 240 242 245 250 256 258 260 265 268 270
## 5 1 1 2 4 1 2 5 1 2 2 5 2 1 4 1 2 3 1 1 1 2 9 1 1 12 1 1 2 2 1 8
## 274 275 280 285 286 287 288 290 292 294 295 296 299 300 301 302 306 307 308 309 311 313 314 315 316 317 318 320 322 323 324 328
## 1 1 14 4 1 1 3 3 2 6 2 3 1 47 3 1 1 1 3 2 1 1 1 4 1 2 2 7 1 2 2 2
## 329 330 332 333 334 335 336 340 341 342 343 345 349 350 351 352 353 354 356 358 359 360 361 362 364 365 366 367 368 370 371 372
## 2 9 4 3 1 5 2 6 1 3 1 3 1 20 1 2 1 1 2 2 1 12 2 1 2 1 2 1 1 3 1 2
## 375 376 378 380 382 384 385 386 390 391 392 394 395 396 398 399 400 401 402 405 406 407 408 410 412 413 414 415 416 420 421 422
## 1 2 4 5 2 2 2 2 3 2 2 1 2 1 1 1 32 3 2 3 1 1 1 3 1 3 2 3 1 16 1 1
## 424 425 426 428 429 430 431 432 434 435 436 440 441 444 448 450 451 454 455 459 460 461 462 466 468 469 470 471 472 474 475 476
## 1 1 3 2 1 8 1 1 1 2 3 10 3 3 3 14 1 1 6 1 1 2 6 1 1 1 8 2 3 2 4 3
## 478 480 481 483 485 486 487 488 490 491 492 494 495 496 498 500 502 503 504 505 506 507 508 509 510 511 512 513 518 520 522 523
## 1 7 1 3 2 3 1 1 6 2 1 1 2 2 3 89 1 3 3 1 1 1 2 1 5 2 1 2 1 11 1 1
## 525 526 530 532 534 535 537 538 539 540 541 544 546 548 550 552 554 555 556 559 560 561 565 566 567 568 569 570 572 574 575 576
## 3 2 4 4 1 3 1 1 2 9 1 1 5 1 13 1 1 2 2 2 19 1 1 3 1 2 2 3 1 4 2 1
## 577 578 580 581 582 583 585 587 588 589 590 591 592 595 596 597 598 600 601 602 605 606 607 608 609 610 612 614 616 617 618 620
## 1 1 8 3 2 3 4 1 7 2 7 2 1 2 3 1 3 27 1 3 1 1 1 1 1 5 1 1 3 1 2 5
## 621 623 624 625 626 628 630 631 632 634 635 636 637 638 640 642 644 645 646 647 649 650 651 652 654 658 660 661 662 664 666 669
## 1 1 2 1 1 4 11 1 2 1 3 2 1 1 9 1 4 2 2 3 1 7 2 3 1 3 4 1 1 1 1 1
## 670 672 674 675 676 679 680 682 683 684 686 687 688 689 690 692 693 694 695 696 698 700 702 705 707 708 709 710 711 712 713 714
## 5 4 2 2 2 4 9 1 1 2 2 1 1 1 4 2 2 2 1 1 2 81 1 4 1 2 1 7 2 3 1 1
## 715 716 718 720 721 722 723 724 726 728 730 731 732 734 735 736 738 740 742 746 747 750 752 753 756 760 764 765 768 770 771 772
## 1 3 1 6 1 1 1 1 2 7 7 1 3 1 1 2 3 7 4 2 3 5 1 2 4 5 2 2 1 10 1 2
## 773 777 778 780 781 783 784 785 787 788 790 791 792 794 796 798 800 802 805 808 809 810 812 813 814 815 819 820 822 824 826 828
## 1 2 2 5 1 1 3 1 1 2 7 1 1 2 2 3 26 1 1 2 1 6 3 2 1 1 2 3 1 1 1 3
## 830 835 838 840 844 845 847 848 849 850 852 854 856 860 864 865 867 868 870 872 875 878 879 880 882 884 885 886 890 893 894 895
## 7 4 3 21 1 1 1 1 1 5 2 1 1 5 1 1 1 1 4 2 4 1 1 5 10 1 1 1 1 1 3 2
## 896 898 900 902 903 910 912 914 915 918 920 924 925 928 930 931 936 937 938 940 941 942 945 946 948 950 953 955 956 958 959 960
## 1 1 14 2 4 6 2 1 2 1 3 6 3 2 2 1 2 1 2 7 1 3 8 1 1 5 1 2 3 1 1 8
## 965 966 967 970 973 974 975 976 978 980 981 982 986 987 988 989 990 994 996 998 999 1000 1005 1008 1013 1014 1015 1016 1017 1018 1019 1020
## 1 4 1 2 1 1 1 2 3 8 1 1 2 1 2 1 3 1 1 1 1 68 1 2 1 1 3 1 1 1 1 5
## 1022 1023 1024 1025 1027 1028 1029 1030 1031 1032 1036 1040 1043 1044 1045 1050 1053 1060 1062 1067 1070 1072 1074 1075 1078 1082 1084 1085 1086 1090 1092 1093
## 5 1 1 1 1 3 1 2 2 2 1 3 1 1 2 25 1 4 1 1 2 1 1 1 1 1 1 3 1 4 1 1
## 1095 1096 1100 1101 1104 1105 1109 1118 1120 1122 1127 1129 1130 1135 1138 1140 1143 1146 1148 1150 1155 1160 1162 1163 1165 1170 1173 1174 1176 1182 1183 1187
## 1 1 5 1 1 2 1 1 4 1 1 1 1 1 1 3 1 1 2 2 7 3 2 2 1 1 1 1 2 1 2 1
## 1190 1200 1203 1210 1211 1212 1215 1225 1232 1234 1240 1244 1246 1249 1250 1251 1256 1260 1262 1270 1272 1275 1276 1280 1282 1284 1290 1294 1295 1299 1300 1303
## 5 16 1 2 1 1 1 2 2 1 2 1 1 1 4 1 1 7 2 2 1 1 1 1 1 1 1 1 2 1 2 1
## 1305 1309 1310 1330 1335 1338 1340 1346 1350 1352 1354 1360 1366 1369 1370 1393 1394 1395 1400 1424 1427 1450 1458 1460 1470 1500 1512 1523 1539 1540 1545 1550
## 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 21 1 1 3 1 2 1 32 1 1 1 3 1 1
## 1570 1572 1575 1586 1590 1595 1596 1600 1648 1650 1660 1680 1700 1708 1710 1715 1724 1745 1748 1750 1756 1787 1800 1810 1850 1880 1918 2000 2002 2010 2050 2100
## 1 1 1 1 1 1 1 1 1 1 1 1 4 1 1 1 2 1 1 6 1 1 2 1 3 1 1 15 1 1 1 3
## 2140 2160 2200 2220 2226 2280 2300 2461 2500 2508 2640 2646 2750 2800 2824 2940 2980 3000 3070 3415 3440 3700 3780 4145 5000 6115 10000 12880 <NA>
## 1 1 1 1 1 1 1 1 6 1 1 1 1 3 1 1 1 4 1 1 1 1 1 1 1 1 1 1 5
## [1] "Frequency table after encoding"
## eh_s10q2. Q711: In the last 7 days how much did the household spend on Bread and Cereals?
## -998 0 1 4 12 18 20 24 25 28 30 35 36 38
## 1 6 3 1 3 2 7 2 1 1 4 2 2 1
## 40 44 45 48 50 52 54 55 56 60 63 64 67 68
## 5 2 1 1 9 1 1 1 1 11 1 1 1 3
## 70 72 76 80 84 85 88 89 90 96 98 100 104 105
## 4 2 1 4 1 2 1 1 6 1 2 18 2 1
## 108 112 113 114 120 124 125 126 128 129 130 131 132 134
## 2 1 1 1 6 1 1 1 1 1 6 1 2 1
## 135 140 142 143 144 145 147 149 150 151 152 154 158 159
## 4 14 1 1 1 1 2 1 9 1 1 3 1 1
## 160 161 164 165 167 168 170 171 175 176 177 178 180 182
## 6 2 1 1 1 1 1 1 2 1 1 1 7 2
## 183 185 188 189 190 194 195 196 199 200 203 205 210 211
## 1 3 1 1 2 1 1 1 1 40 2 4 5 1
## 213 214 215 216 218 220 221 222 224 225 226 227 230 232
## 1 2 4 1 2 5 1 2 2 5 2 1 4 1
## 233 234 235 236 237 238 240 242 245 250 256 258 260 265
## 2 3 1 1 1 2 9 1 1 12 1 1 2 2
## 268 270 274 275 280 285 286 287 288 290 292 294 295 296
## 1 8 1 1 14 4 1 1 3 3 2 6 2 3
## 299 300 301 302 306 307 308 309 311 313 314 315 316 317
## 1 47 3 1 1 1 3 2 1 1 1 4 1 2
## 318 320 322 323 324 328 329 330 332 333 334 335 336 340
## 2 7 1 2 2 2 2 9 4 3 1 5 2 6
## 341 342 343 345 349 350 351 352 353 354 356 358 359 360
## 1 3 1 3 1 20 1 2 1 1 2 2 1 12
## 361 362 364 365 366 367 368 370 371 372 375 376 378 380
## 2 1 2 1 2 1 1 3 1 2 1 2 4 5
## 382 384 385 386 390 391 392 394 395 396 398 399 400 401
## 2 2 2 2 3 2 2 1 2 1 1 1 32 3
## 402 405 406 407 408 410 412 413 414 415 416 420 421 422
## 2 3 1 1 1 3 1 3 2 3 1 16 1 1
## 424 425 426 428 429 430 431 432 434 435 436 440 441 444
## 1 1 3 2 1 8 1 1 1 2 3 10 3 3
## 448 450 451 454 455 459 460 461 462 466 468 469 470 471
## 3 14 1 1 6 1 1 2 6 1 1 1 8 2
## 472 474 475 476 478 480 481 483 485 486 487 488 490 491
## 3 2 4 3 1 7 1 3 2 3 1 1 6 2
## 492 494 495 496 498 500 502 503 504 505 506 507 508 509
## 1 1 2 2 3 89 1 3 3 1 1 1 2 1
## 510 511 512 513 518 520 522 523 525 526 530 532 534 535
## 5 2 1 2 1 11 1 1 3 2 4 4 1 3
## 537 538 539 540 541 544 546 548 550 552 554 555 556 559
## 1 1 2 9 1 1 5 1 13 1 1 2 2 2
## 560 561 565 566 567 568 569 570 572 574 575 576 577 578
## 19 1 1 3 1 2 2 3 1 4 2 1 1 1
## 580 581 582 583 585 587 588 589 590 591 592 595 596 597
## 8 3 2 3 4 1 7 2 7 2 1 2 3 1
## 598 600 601 602 605 606 607 608 609 610 612 614 616 617
## 3 27 1 3 1 1 1 1 1 5 1 1 3 1
## 618 620 621 623 624 625 626 628 630 631 632 634 635 636
## 2 5 1 1 2 1 1 4 11 1 2 1 3 2
## 637 638 640 642 644 645 646 647 649 650 651 652 654 658
## 1 1 9 1 4 2 2 3 1 7 2 3 1 3
## 660 661 662 664 666 669 670 672 674 675 676 679 680 682
## 4 1 1 1 1 1 5 4 2 2 2 4 9 1
## 683 684 686 687 688 689 690 692 693 694 695 696 698 700
## 1 2 2 1 1 1 4 2 2 2 1 1 2 81
## 702 705 707 708 709 710 711 712 713 714 715 716 718 720
## 1 4 1 2 1 7 2 3 1 1 1 3 1 6
## 721 722 723 724 726 728 730 731 732 734 735 736 738 740
## 1 1 1 1 2 7 7 1 3 1 1 2 3 7
## 742 746 747 750 752 753 756 760 764 765 768 770 771 772
## 4 2 3 5 1 2 4 5 2 2 1 10 1 2
## 773 777 778 780 781 783 784 785 787 788 790 791 792 794
## 1 2 2 5 1 1 3 1 1 2 7 1 1 2
## 796 798 800 802 805 808 809 810 812 813 814 815 819 820
## 2 3 26 1 1 2 1 6 3 2 1 1 2 3
## 822 824 826 828 830 835 838 840 844 845 847 848 849 850
## 1 1 1 3 7 4 3 21 1 1 1 1 1 5
## 852 854 856 860 864 865 867 868 870 872 875 878 879 880
## 2 1 1 5 1 1 1 1 4 2 4 1 1 5
## 882 884 885 886 890 893 894 895 896 898 900 902 903 910
## 10 1 1 1 1 1 3 2 1 1 14 2 4 6
## 912 914 915 918 920 924 925 928 930 931 936 937 938 940
## 2 1 2 1 3 6 3 2 2 1 2 1 2 7
## 941 942 945 946 948 950 953 955 956 958 959 960 965 966
## 1 3 8 1 1 5 1 2 3 1 1 8 1 4
## 967 970 973 974 975 976 978 980 981 982 986 987 988 989
## 1 2 1 1 1 2 3 8 1 1 2 1 2 1
## 990 994 996 998 999 1000 1005 1008 1013 1014 1015 1016 1017 1018
## 3 1 1 1 1 68 1 2 1 1 3 1 1 1
## 1019 1020 1022 1023 1024 1025 1027 1028 1029 1030 1031 1032 1036 1040
## 1 5 5 1 1 1 1 3 1 2 2 2 1 3
## 1043 1044 1045 1050 1053 1060 1062 1067 1070 1072 1074 1075 1078 1082
## 1 1 2 25 1 4 1 1 2 1 1 1 1 1
## 1084 1085 1086 1090 1092 1093 1095 1096 1100 1101 1104 1105 1109 1118
## 1 3 1 4 1 1 1 1 5 1 1 2 1 1
## 1120 1122 1127 1129 1130 1135 1138 1140 1143 1146 1148 1150 1155 1160
## 4 1 1 1 1 1 1 3 1 1 2 2 7 3
## 1162 1163 1165 1170 1173 1174 1176 1182 1183 1187 1190 1200 1203 1210
## 2 2 1 1 1 1 2 1 2 1 5 16 1 2
## 1211 1212 1215 1225 1232 1234 1240 1244 1246 1249 1250 1251 1256 1260
## 1 1 1 2 2 1 2 1 1 1 4 1 1 7
## 1262 1270 1272 1275 1276 1280 1282 1284 1290 1294 1295 1299 1300 1303
## 2 2 1 1 1 1 1 1 1 1 2 1 2 1
## 1305 1309 1310 1330 1335 1338 1340 1346 1350 1352 1354 1360 1366 1369
## 1 1 2 2 1 1 1 1 1 1 1 1 1 1
## 1370 1393 1394 1395 1400 1424 1427 1450 1458 1460 1470 1500 1512 1523
## 2 1 1 1 21 1 1 3 1 2 1 32 1 1
## 1539 1540 1545 1550 1570 1572 1575 1586 1590 1595 1596 1600 1648 1650
## 1 3 1 1 1 1 1 1 1 1 1 1 1 1
## 1660 1680 1700 1708 1710 1715 1724 1745 1748 1750 1756 1787 1800 1810
## 1 1 4 1 1 1 2 1 1 6 1 1 2 1
## 1850 1880 1918 2000 2002 2010 2050 2100 2140 2160 2200 2220 2226 2280
## 3 1 1 15 1 1 1 3 1 1 1 1 1 1
## 2300 2461 2500 2508 2640 2646 2750 2800 2824 2940 2980 3000 or more <NA>
## 1 1 6 1 1 1 1 3 1 1 1 14 5
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q4)[na.exclude(mydata$eh_s10q4)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q4", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q4. Q713: In the last 7 days how much did the household spend on Roots and tubers?
## -998 0 2 5 6 7 8 10 11 12 14 15 16 17 18 20 21 22 23 24 25 28 30 32 35 36 40 42 45 46 48 49 50 54 55 60 64 65
## 1 236 1 4 1 2 2 43 1 4 2 25 1 2 4 132 1 1 1 2 32 1 89 2 26 3 68 1 13 1 2 1 108 2 4 48 1 7
## 66 67 69 70 75 78 80 84 85 90 95 100 105 110 120 125 130 135 140 145 150 160 170 180 184 200 210 215 235 240 250 270 288 290 295 300 328 350
## 1 1 1 13 5 1 22 1 2 16 4 92 4 4 5 1 6 1 3 1 28 4 2 3 1 45 3 1 1 1 4 2 1 1 1 16 1 1
## 400 409 420 500 525 600 635 700 870 1000 1050 <NA>
## 1 1 1 6 1 1 1 1 1 3 1 1095
## [1] "Frequency table after encoding"
## eh_s10q4. Q713: In the last 7 days how much did the household spend on Roots and tubers?
## -998 0 2 5 6 7 8 10 11 12 14 15 16 17 18 20
## 1 236 1 4 1 2 2 43 1 4 2 25 1 2 4 132
## 21 22 23 24 25 28 30 32 35 36 40 42 45 46 48 49
## 1 1 1 2 32 1 89 2 26 3 68 1 13 1 2 1
## 50 54 55 60 64 65 66 67 69 70 75 78 80 84 85 90
## 108 2 4 48 1 7 1 1 1 13 5 1 22 1 2 16
## 95 100 105 110 120 125 130 135 140 145 150 160 170 180 184 200
## 4 92 4 4 5 1 6 1 3 1 28 4 2 3 1 45
## 210 215 235 240 250 270 288 290 295 300 328 350 400 409 420 500
## 3 1 1 1 4 2 1 1 1 16 1 1 1 1 1 6
## 525 600 635 637 or more <NA>
## 1 1 1 6 1095
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q6)[na.exclude(mydata$eh_s10q6)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q6", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q6. Q715: In the last 7 days how much did the household spend on Vegetables? Cabbag
## -998 0 1 4 5 7 8 10 12 14 15 16 17 18 20 22 23 24 25 27 28 30 31 34 35 36 37 39 40 41 42 43 44 45 46 47 49 50
## 2 216 1 1 5 1 1 30 5 3 14 1 2 2 90 1 1 4 18 2 1 77 1 1 18 1 4 2 62 2 1 4 2 22 1 2 1 168
## 51 54 55 56 58 60 62 63 64 65 66 68 69 70 71 72 74 75 76 78 79 80 81 83 84 85 86 87 88 89 90 93 95 96 99 100 101 105
## 3 1 12 6 2 65 1 3 2 8 1 1 1 48 1 2 1 21 1 1 1 42 2 1 2 15 3 1 1 1 30 2 15 1 2 227 1 12
## 108 109 110 111 113 114 115 117 119 120 123 124 125 126 128 130 132 133 134 135 140 141 142 144 145 146 148 150 152 153 155 156 160 162 163 165 168 170
## 1 2 15 1 1 2 2 1 1 33 2 1 11 1 1 17 1 4 1 6 31 1 2 1 7 1 1 104 1 1 6 1 12 1 1 3 1 9
## 172 175 178 180 181 184 185 190 193 195 196 197 200 205 210 211 213 214 215 219 220 225 230 235 240 241 245 250 255 260 261 268 270 274 275 280 285 290
## 1 10 1 9 2 1 4 7 2 5 1 2 175 1 9 1 1 1 6 1 6 6 8 2 7 1 4 23 2 2 1 2 8 1 2 8 2 2
## 295 297 300 309 310 315 320 326 330 335 340 345 350 356 375 380 385 390 395 400 415 420 425 435 445 447 450 465 475 480 490 500 530 550 570 600 645 700
## 2 1 112 1 2 1 3 1 4 2 2 1 19 1 1 1 2 2 1 24 1 6 1 1 1 1 1 2 1 1 1 41 1 1 1 6 1 12
## 750 840 898 980 1000 1400 1500 1750 <NA>
## 1 1 1 1 5 1 3 1 86
## [1] "Frequency table after encoding"
## eh_s10q6. Q715: In the last 7 days how much did the household spend on Vegetables? Cabbag
## -998 0 1 4 5 7 8 10 12 14 15 16 17 18 20 22
## 2 216 1 1 5 1 1 30 5 3 14 1 2 2 90 1
## 23 24 25 27 28 30 31 34 35 36 37 39 40 41 42 43
## 1 4 18 2 1 77 1 1 18 1 4 2 62 2 1 4
## 44 45 46 47 49 50 51 54 55 56 58 60 62 63 64 65
## 2 22 1 2 1 168 3 1 12 6 2 65 1 3 2 8
## 66 68 69 70 71 72 74 75 76 78 79 80 81 83 84 85
## 1 1 1 48 1 2 1 21 1 1 1 42 2 1 2 15
## 86 87 88 89 90 93 95 96 99 100 101 105 108 109 110 111
## 3 1 1 1 30 2 15 1 2 227 1 12 1 2 15 1
## 113 114 115 117 119 120 123 124 125 126 128 130 132 133 134 135
## 1 2 2 1 1 33 2 1 11 1 1 17 1 4 1 6
## 140 141 142 144 145 146 148 150 152 153 155 156 160 162 163 165
## 31 1 2 1 7 1 1 104 1 1 6 1 12 1 1 3
## 168 170 172 175 178 180 181 184 185 190 193 195 196 197 200 205
## 1 9 1 10 1 9 2 1 4 7 2 5 1 2 175 1
## 210 211 213 214 215 219 220 225 230 235 240 241 245 250 255 260
## 9 1 1 1 6 1 6 6 8 2 7 1 4 23 2 2
## 261 268 270 274 275 280 285 290 295 297 300 309 310 315 320 326
## 1 2 8 1 2 8 2 2 2 1 112 1 2 1 3 1
## 330 335 340 345 350 356 375 380 385 390 395 400 415 420 425 435
## 4 2 2 1 19 1 1 1 2 2 1 24 1 6 1 1
## 445 447 450 465 475 480 490 500 530 550 570 600 645 700 750 840
## 1 1 1 2 1 1 1 41 1 1 1 6 1 12 1 1
## 897 or more <NA>
## 12 86
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q8)[na.exclude(mydata$eh_s10q8)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q8", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q8. Q717: In the last 7 days how much did the household spend on Meat? Beef Pork Go
## -998 0 12 16 20 22 24 25 30 33 35 37 40 41 42 45 50 55 59 60 64 65 70 75 76 78 80
## 1 67 1 1 3 1 2 4 9 1 4 1 10 1 2 5 20 4 1 13 1 1 12 10 1 1 15
## 81 84 85 90 92 95 96 100 103 105 106 108 110 115 118 120 125 130 133 135 136 138 139 140 142 144 145
## 1 1 6 16 1 4 1 97 1 6 1 2 25 3 1 40 6 16 1 2 1 1 1 16 1 2 2
## 150 155 156 160 161 162 164 165 168 170 171 175 180 185 186 188 190 195 196 200 201 203 205 207 209 210 214
## 76 4 1 25 1 1 1 7 2 18 1 8 55 5 1 1 16 4 1 206 1 1 4 1 1 26 1
## 215 217 220 225 228 230 232 235 236 239 240 245 246 250 255 260 262 265 270 273 275 278 280 285 288 290 295
## 3 1 64 4 2 27 1 4 1 1 19 1 1 38 4 8 1 5 8 1 8 2 19 3 1 3 2
## 300 304 309 310 315 320 321 325 327 330 331 333 335 340 345 350 354 355 357 360 365 366 370 375 380 385 390
## 91 1 1 7 7 12 1 3 1 8 1 2 2 13 3 35 1 1 1 28 2 1 6 4 17 1 5
## 391 400 405 410 415 420 424 425 428 430 432 435 440 445 446 450 455 460 465 466 468 470 476 480 490 497 500
## 1 92 1 9 3 13 1 1 1 2 1 1 11 2 2 13 1 3 2 1 1 8 1 10 1 1 86
## 502 510 514 515 517 520 525 530 531 538 540 550 555 560 570 572 575 580 590 600 606 610 620 630 635 640 645
## 1 5 1 2 1 7 2 4 1 1 11 5 1 4 4 1 1 2 1 37 1 2 1 6 1 3 1
## 650 660 670 700 709 710 715 720 730 740 745 750 760 770 780 790 800 810 820 830 840 850 860 875 885 900 930
## 3 6 2 14 1 3 1 6 1 3 1 6 2 3 4 2 8 3 1 1 2 1 1 2 1 3 1
## 960 965 980 990 1000 1005 1010 1015 1020 1040 1050 1080 1105 1140 1157 1200 1210 1235 1240 1290 1300 1320 1345 1400 1420 1440 1500
## 1 1 3 1 23 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 3 6
## 1540 1550 1600 1680 1800 1880 1900 2000 2010 2080 2100 2500 3300 5250 6000 6980 7000 240120 <NA>
## 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 347
## [1] "Frequency table after encoding"
## eh_s10q8. Q717: In the last 7 days how much did the household spend on Meat? Beef Pork Go
## -998 0 12 16 20 22 24 25 30 33 35 37 40 41
## 1 67 1 1 3 1 2 4 9 1 4 1 10 1
## 42 45 50 55 59 60 64 65 70 75 76 78 80 81
## 2 5 20 4 1 13 1 1 12 10 1 1 15 1
## 84 85 90 92 95 96 100 103 105 106 108 110 115 118
## 1 6 16 1 4 1 97 1 6 1 2 25 3 1
## 120 125 130 133 135 136 138 139 140 142 144 145 150 155
## 40 6 16 1 2 1 1 1 16 1 2 2 76 4
## 156 160 161 162 164 165 168 170 171 175 180 185 186 188
## 1 25 1 1 1 7 2 18 1 8 55 5 1 1
## 190 195 196 200 201 203 205 207 209 210 214 215 217 220
## 16 4 1 206 1 1 4 1 1 26 1 3 1 64
## 225 228 230 232 235 236 239 240 245 246 250 255 260 262
## 4 2 27 1 4 1 1 19 1 1 38 4 8 1
## 265 270 273 275 278 280 285 288 290 295 300 304 309 310
## 5 8 1 8 2 19 3 1 3 2 91 1 1 7
## 315 320 321 325 327 330 331 333 335 340 345 350 354 355
## 7 12 1 3 1 8 1 2 2 13 3 35 1 1
## 357 360 365 366 370 375 380 385 390 391 400 405 410 415
## 1 28 2 1 6 4 17 1 5 1 92 1 9 3
## 420 424 425 428 430 432 435 440 445 446 450 455 460 465
## 13 1 1 1 2 1 1 11 2 2 13 1 3 2
## 466 468 470 476 480 490 497 500 502 510 514 515 517 520
## 1 1 8 1 10 1 1 86 1 5 1 2 1 7
## 525 530 531 538 540 550 555 560 570 572 575 580 590 600
## 2 4 1 1 11 5 1 4 4 1 1 2 1 37
## 606 610 620 630 635 640 645 650 660 670 700 709 710 715
## 1 2 1 6 1 3 1 3 6 2 14 1 3 1
## 720 730 740 745 750 760 770 780 790 800 810 820 830 840
## 6 1 3 1 6 2 3 4 2 8 3 1 1 2
## 850 860 875 885 900 930 960 965 980 990 1000 1005 1010 1015
## 1 1 2 1 3 1 1 1 3 1 23 1 1 1
## 1020 1040 1050 1080 1105 1140 1157 1200 1210 1235 1240 1290 1300 1320
## 1 1 1 1 1 1 1 3 1 1 1 1 1 1
## 1345 1400 1420 1440 1500 1540 1550 1600 1680 1800 1880 1900 2000 2002 or more
## 1 1 1 3 6 1 1 1 1 2 1 1 2 10
## <NA>
## 347
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q10)[na.exclude(mydata$eh_s10q10)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q10", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q10. Q719: In the last 7 days how much did the household spend on Fish? Fresh Fish D
## -998 0 1 10 12 15 16 17 18 19 20 25 26 27 28 29 30 32 34 35 36 37 38 40 42 43 45 48 49 50 51 52 54 55 56 57 58 59
## 1 129 2 3 1 4 3 4 11 1 18 6 2 1 1 1 15 3 4 4 8 1 5 27 2 1 4 6 1 60 3 6 7 4 4 2 1 2
## 60 61 62 63 64 65 66 67 68 70 71 72 74 75 77 78 79 80 81 82 83 84 85 87 88 90 92 93 95 96 98 99 100 101 102 104 105 106
## 55 1 1 2 3 7 3 1 3 35 3 2 3 15 4 5 2 26 1 1 1 10 3 1 1 21 2 2 4 4 3 2 112 1 2 2 10 1
## 107 108 110 111 112 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 130 131 132 134 135 136 137 138 140 142 144 145 146 147 148 149 150 151
## 1 1 17 1 1 3 4 2 1 4 2 86 2 1 2 4 6 4 3 3 39 2 1 6 2 3 2 3 33 1 2 2 1 3 3 1 98 1
## 152 153 154 155 156 157 158 160 161 163 164 165 166 167 168 170 172 174 175 176 177 178 180 181 182 184 185 186 187 188 189 190 192 193 194 195 196 197
## 2 1 1 1 6 1 4 23 2 2 2 3 6 1 1 9 2 4 5 4 2 3 33 2 1 5 5 6 1 4 2 5 3 1 3 2 4 3
## 198 200 201 202 203 204 205 206 208 209 210 211 212 215 216 217 218 219 220 222 223 225 226 227 229 230 231 232 233 234 235 236 238 240 242 243 244 245
## 5 143 2 4 1 3 5 1 2 4 17 1 2 2 6 2 2 1 14 4 1 2 2 1 1 10 2 1 1 1 5 4 1 28 1 1 5 1
## 246 250 251 252 253 255 256 257 259 260 261 262 267 268 270 272 274 275 276 280 282 284 286 288 290 292 295 296 297 300 302 304 308 310 312 318 320 322
## 1 27 2 2 1 2 3 1 1 17 2 1 1 1 7 2 2 1 1 14 3 2 1 1 4 2 1 1 1 139 1 1 1 2 2 1 11 1
## 325 326 328 330 334 336 338 340 342 344 345 346 348 350 352 354 355 360 364 369 370 374 380 385 386 389 390 392 394 395 396 398 400 401 410 414 415 416
## 1 2 1 7 1 2 1 7 1 1 2 1 1 22 1 4 1 18 1 1 4 1 8 1 2 1 9 2 2 1 1 1 46 1 4 1 1 1
## 420 422 428 430 431 434 435 440 444 445 450 455 460 461 463 467 473 475 480 484 485 490 495 500 504 509 510 518 520 530 540 550 556 558 560 574 580 586
## 4 1 1 3 1 1 1 2 1 2 9 1 4 1 1 1 1 1 7 1 1 4 1 73 1 1 3 1 1 2 1 2 1 1 7 1 1 1
## 598 600 620 630 644 650 660 690 695 700 718 720 750 760 770 780 800 802 858 942 960 1000 1020 1050 1092 1120 1190 1200 1300 1329 1400 2030 2100 2574 3003 3500 7000 <NA>
## 1 13 2 1 1 5 1 1 1 25 1 1 1 2 2 1 7 1 1 1 1 8 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1 101
## [1] "Frequency table after encoding"
## eh_s10q10. Q719: In the last 7 days how much did the household spend on Fish? Fresh Fish D
## -998 0 1 10 12 15 16 17 18 19 20 25 26 27
## 1 129 2 3 1 4 3 4 11 1 18 6 2 1
## 28 29 30 32 34 35 36 37 38 40 42 43 45 48
## 1 1 15 3 4 4 8 1 5 27 2 1 4 6
## 49 50 51 52 54 55 56 57 58 59 60 61 62 63
## 1 60 3 6 7 4 4 2 1 2 55 1 1 2
## 64 65 66 67 68 70 71 72 74 75 77 78 79 80
## 3 7 3 1 3 35 3 2 3 15 4 5 2 26
## 81 82 83 84 85 87 88 90 92 93 95 96 98 99
## 1 1 1 10 3 1 1 21 2 2 4 4 3 2
## 100 101 102 104 105 106 107 108 110 111 112 114 115 116
## 112 1 2 2 10 1 1 1 17 1 1 3 4 2
## 117 118 119 120 121 122 123 124 125 126 127 128 130 131
## 1 4 2 86 2 1 2 4 6 4 3 3 39 2
## 132 134 135 136 137 138 140 142 144 145 146 147 148 149
## 1 6 2 3 2 3 33 1 2 2 1 3 3 1
## 150 151 152 153 154 155 156 157 158 160 161 163 164 165
## 98 1 2 1 1 1 6 1 4 23 2 2 2 3
## 166 167 168 170 172 174 175 176 177 178 180 181 182 184
## 6 1 1 9 2 4 5 4 2 3 33 2 1 5
## 185 186 187 188 189 190 192 193 194 195 196 197 198 200
## 5 6 1 4 2 5 3 1 3 2 4 3 5 143
## 201 202 203 204 205 206 208 209 210 211 212 215 216 217
## 2 4 1 3 5 1 2 4 17 1 2 2 6 2
## 218 219 220 222 223 225 226 227 229 230 231 232 233 234
## 2 1 14 4 1 2 2 1 1 10 2 1 1 1
## 235 236 238 240 242 243 244 245 246 250 251 252 253 255
## 5 4 1 28 1 1 5 1 1 27 2 2 1 2
## 256 257 259 260 261 262 267 268 270 272 274 275 276 280
## 3 1 1 17 2 1 1 1 7 2 2 1 1 14
## 282 284 286 288 290 292 295 296 297 300 302 304 308 310
## 3 2 1 1 4 2 1 1 1 139 1 1 1 2
## 312 318 320 322 325 326 328 330 334 336 338 340 342 344
## 2 1 11 1 1 2 1 7 1 2 1 7 1 1
## 345 346 348 350 352 354 355 360 364 369 370 374 380 385
## 2 1 1 22 1 4 1 18 1 1 4 1 8 1
## 386 389 390 392 394 395 396 398 400 401 410 414 415 416
## 2 1 9 2 2 1 1 1 46 1 4 1 1 1
## 420 422 428 430 431 434 435 440 444 445 450 455 460 461
## 4 1 1 3 1 1 1 2 1 2 9 1 4 1
## 463 467 473 475 480 484 485 490 495 500 504 509 510 518
## 1 1 1 1 7 1 1 4 1 73 1 1 3 1
## 520 530 540 550 556 558 560 574 580 586 598 600 620 630
## 1 2 1 2 1 1 7 1 1 1 1 13 2 1
## 644 650 660 690 695 700 718 720 750 760 770 780 800 802
## 1 5 1 1 1 25 1 1 1 2 2 1 7 1
## 858 942 960 1000 1020 1050 1092 1120 1190 or more <NA>
## 1 1 1 8 1 2 1 1 12 101
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q12)[na.exclude(mydata$eh_s10q12)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q12", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q12. Q721: In the last 7 days how much did the household spend on Dairy products and
## -998 0 5 6 7 8 10 12 14 15 16 17 18 20 21 22 24 25 26 27 28 30 32 33 34 35 36 38 39 40 41 42 43 44 45 46 47 48
## 1 41 1 2 1 1 1 8 24 5 1 1 23 21 49 5 33 9 3 1 54 43 10 1 2 42 30 2 1 39 2 40 2 5 10 1 1 12
## 49 50 52 54 55 56 58 59 60 61 62 63 64 65 66 67 68 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 9 66 1 7 7 17 2 1 57 2 4 12 6 5 5 2 4 48 1 14 3 3 14 2 3 3 1 23 1 3 1 23 2 2 2 3 1 28
## 91 92 93 94 95 96 97 98 99 100 102 103 104 105 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130
## 1 5 3 4 4 5 1 10 2 102 2 3 3 23 3 2 6 14 1 8 3 2 3 2 1 1 5 25 1 5 1 3 7 12 2 1 1 16
## 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 160 161 162 163 164 165 166 167 168 169
## 1 1 2 3 7 3 2 1 2 26 2 3 1 4 8 1 6 3 2 70 1 7 2 3 4 2 3 1 17 1 2 1 4 6 4 1 12 2
## 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 187 188 190 192 194 195 196 197 200 201 203 205 207 208 209 210 212 213 214 215 216 217
## 6 2 2 3 3 7 1 3 1 2 18 1 2 2 2 3 1 1 4 4 1 2 8 1 62 1 2 3 3 1 1 11 7 1 1 3 1 1
## 219 220 221 223 224 225 229 230 232 233 234 235 236 238 239 240 243 244 245 246 247 250 251 252 255 256 258 259 260 262 263 264 266 268 270 272 273 274
## 1 10 1 1 4 3 2 2 1 1 3 1 2 1 1 6 1 2 9 1 2 12 1 3 1 1 1 2 3 1 1 1 2 1 3 2 1 1
## 275 276 277 278 279 280 283 285 286 287 290 294 295 298 299 300 301 302 303 305 306 307 309 313 314 315 319 320 325 330 335 336 340 343 346 348 349 350
## 2 1 1 2 2 5 1 1 2 1 1 2 1 2 1 55 2 1 1 1 1 1 1 1 1 2 1 4 1 2 3 2 4 1 3 2 1 18
## 351 360 364 375 378 380 384 385 392 395 399 400 402 404 408 410 414 416 420 421 423 427 428 430 440 445 448 450 455 456 457 460 468 469 472 480 490 495
## 1 4 2 1 2 1 1 1 1 1 1 17 1 1 1 1 1 1 3 2 1 1 1 2 2 1 1 6 1 1 1 2 1 1 2 2 4 1
## 498 500 506 507 508 524 526 530 532 535 540 542 550 560 564 567 570 572 582 591 595 600 607 610 620 636 642 656 660 700 710 713 740 741 742 746 755 760
## 1 14 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 6 1 1 2 1 1 1 1 7 1 1 1 1 1 1 1 1
## 809 820 838 850 880 882 905 972 1000 1050 1060 1070 1084 1096 1150 1165 1200 1216 1236 1400 1421 1494 1500 1558 1600 1690 1700 1720 2043 2100 2480 3800 <NA>
## 1 2 1 1 1 1 1 1 8 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 271
## [1] "Frequency table after encoding"
## eh_s10q12. Q721: In the last 7 days how much did the household spend on Dairy products and
## -998 0 5 6 7 8 10 12 14 15 16 17 18 20
## 1 41 1 2 1 1 1 8 24 5 1 1 23 21
## 21 22 24 25 26 27 28 30 32 33 34 35 36 38
## 49 5 33 9 3 1 54 43 10 1 2 42 30 2
## 39 40 41 42 43 44 45 46 47 48 49 50 52 54
## 1 39 2 40 2 5 10 1 1 12 9 66 1 7
## 55 56 58 59 60 61 62 63 64 65 66 67 68 70
## 7 17 2 1 57 2 4 12 6 5 5 2 4 48
## 71 72 73 74 75 76 77 78 79 80 81 82 83 84
## 1 14 3 3 14 2 3 3 1 23 1 3 1 23
## 85 86 87 88 89 90 91 92 93 94 95 96 97 98
## 2 2 2 3 1 28 1 5 3 4 4 5 1 10
## 99 100 102 103 104 105 107 108 109 110 111 112 113 114
## 2 102 2 3 3 23 3 2 6 14 1 8 3 2
## 115 116 117 118 119 120 121 122 123 124 125 126 127 128
## 3 2 1 1 5 25 1 5 1 3 7 12 2 1
## 129 130 131 132 133 134 135 136 137 138 139 140 141 142
## 1 16 1 1 2 3 7 3 2 1 2 26 2 3
## 143 144 145 146 147 148 149 150 151 152 153 154 155 156
## 1 4 8 1 6 3 2 70 1 7 2 3 4 2
## 157 158 160 161 162 163 164 165 166 167 168 169 170 171
## 3 1 17 1 2 1 4 6 4 1 12 2 6 2
## 172 173 174 175 176 177 178 179 180 181 182 183 184 185
## 2 3 3 7 1 3 1 2 18 1 2 2 2 3
## 187 188 190 192 194 195 196 197 200 201 203 205 207 208
## 1 1 4 4 1 2 8 1 62 1 2 3 3 1
## 209 210 212 213 214 215 216 217 219 220 221 223 224 225
## 1 11 7 1 1 3 1 1 1 10 1 1 4 3
## 229 230 232 233 234 235 236 238 239 240 243 244 245 246
## 2 2 1 1 3 1 2 1 1 6 1 2 9 1
## 247 250 251 252 255 256 258 259 260 262 263 264 266 268
## 2 12 1 3 1 1 1 2 3 1 1 1 2 1
## 270 272 273 274 275 276 277 278 279 280 283 285 286 287
## 3 2 1 1 2 1 1 2 2 5 1 1 2 1
## 290 294 295 298 299 300 301 302 303 305 306 307 309 313
## 1 2 1 2 1 55 2 1 1 1 1 1 1 1
## 314 315 319 320 325 330 335 336 340 343 346 348 349 350
## 1 2 1 4 1 2 3 2 4 1 3 2 1 18
## 351 360 364 375 378 380 384 385 392 395 399 400 402 404
## 1 4 2 1 2 1 1 1 1 1 1 17 1 1
## 408 410 414 416 420 421 423 427 428 430 440 445 448 450
## 1 1 1 1 3 2 1 1 1 2 2 1 1 6
## 455 456 457 460 468 469 472 480 490 495 498 500 506 507
## 1 1 1 2 1 1 2 2 4 1 1 14 1 2
## 508 524 526 530 532 535 540 542 550 560 564 567 570 572
## 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## 582 591 595 600 607 610 620 636 642 656 660 700 710 713
## 1 1 2 6 1 1 2 1 1 1 1 7 1 1
## 740 741 742 746 755 760 809 820 838 850 880 882 905 972
## 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 1000 1050 1060 1070 1084 1096 1150 1165 1200 1216 1236 1400 1421 1494
## 8 1 1 1 1 1 1 1 1 1 1 2 1 1
## 1499 or more <NA>
## 11 271
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q14)[na.exclude(mydata$eh_s10q14)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q14", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q14. Q723: In the last 7 days how much did the household spend on Oils and fats ? Bu
## -998 0 3 5 6 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 32 33 34 35 36 37 38 39 40 42
## 1 32 1 14 2 4 2 56 9 16 13 11 43 8 22 60 3 284 10 37 12 51 109 24 14 31 1 124 12 3 15 46 44 3 3 3 177 9
## 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 63 64 65 66 67 68 69 70 72 75 77 78 80 81 84 85 86 88 89 90 91
## 32 14 11 2 17 1 143 12 8 1 17 13 10 3 1 2 110 2 3 7 6 1 3 4 29 15 21 2 5 38 3 12 7 1 7 1 30 1
## 92 94 95 96 98 100 101 102 104 105 107 110 112 114 115 116 119 120 121 125 126 127 129 130 134 135 136 140 144 148 150 154 155 156 157 160 161 164
## 4 1 6 3 2 88 1 1 2 8 1 4 1 1 2 2 1 10 1 2 2 1 1 6 1 2 1 10 1 2 27 1 1 1 1 2 1 4
## 167 168 170 175 176 180 182 184 185 189 190 195 200 210 220 230 237 240 250 280 300 310 350 400 420 548 890 2100 <NA>
## 1 1 7 2 1 4 1 1 1 1 2 1 22 2 1 1 1 2 4 1 9 1 1 3 1 1 1 1 103
## [1] "Frequency table after encoding"
## eh_s10q14. Q723: In the last 7 days how much did the household spend on Oils and fats ? Bu
## -998 0 3 5 6 8 9 10 11 12 13 14 15 16 17 18
## 1 32 1 14 2 4 2 56 9 16 13 11 43 8 22 60
## 19 20 21 22 23 24 25 26 27 28 29 30 32 33 34 35
## 3 284 10 37 12 51 109 24 14 31 1 124 12 3 15 46
## 36 37 38 39 40 42 44 45 46 47 48 49 50 51 52 53
## 44 3 3 3 177 9 32 14 11 2 17 1 143 12 8 1
## 54 55 56 57 58 59 60 63 64 65 66 67 68 69 70 72
## 17 13 10 3 1 2 110 2 3 7 6 1 3 4 29 15
## 75 77 78 80 81 84 85 86 88 89 90 91 92 94 95 96
## 21 2 5 38 3 12 7 1 7 1 30 1 4 1 6 3
## 98 100 101 102 104 105 107 110 112 114 115 116 119 120 121 125
## 2 88 1 1 2 8 1 4 1 1 2 2 1 10 1 2
## 126 127 129 130 134 135 136 140 144 148 150 154 155 156 157 160
## 2 1 1 6 1 2 1 10 1 2 27 1 1 1 1 2
## 161 164 167 168 170 175 176 180 182 184 185 189 190 195 200 210
## 1 4 1 1 7 2 1 4 1 1 1 1 2 1 22 2
## 220 230 237 240 250 280 300 or more <NA>
## 1 1 1 2 4 1 18 103
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q16)[na.exclude(mydata$eh_s10q16)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q16", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q16. Q725: In the last 7 days how much did the household spend on Fruits and nuts? P
## -998 0 5 6 7 8 10 12 15 17 18 20 24 25 27 30 32 33 34 35 38 39 40 42 44 45 47 50 52 53 55 60 65 68 70 72 75 80
## 1 376 8 1 2 2 32 4 15 1 1 79 1 41 1 87 1 2 2 38 1 1 94 1 1 19 1 150 1 1 9 69 7 1 27 1 22 31
## 81 85 90 95 100 105 107 108 110 112 120 125 130 135 138 140 148 150 155 158 160 165 174 175 180 184 185 186 190 195 200 210 215 220 225 230 235 240
## 1 8 16 2 142 4 1 1 10 1 23 7 3 1 1 11 1 44 1 1 9 3 1 3 8 1 2 1 2 2 69 2 1 2 1 3 3 3
## 250 260 265 270 275 300 320 350 360 380 400 415 420 430 490 500 525 545 550 600 700 747 1400 1500 <NA>
## 12 1 1 2 1 31 2 3 1 1 10 1 1 1 1 3 1 1 1 3 1 1 1 1 679
## [1] "Frequency table after encoding"
## eh_s10q16. Q725: In the last 7 days how much did the household spend on Fruits and nuts? P
## -998 0 5 6 7 8 10 12 15 17 18 20 24 25 27 30
## 1 376 8 1 2 2 32 4 15 1 1 79 1 41 1 87
## 32 33 34 35 38 39 40 42 44 45 47 50 52 53 55 60
## 1 2 2 38 1 1 94 1 1 19 1 150 1 1 9 69
## 65 68 70 72 75 80 81 85 90 95 100 105 107 108 110 112
## 7 1 27 1 22 31 1 8 16 2 142 4 1 1 10 1
## 120 125 130 135 138 140 148 150 155 158 160 165 174 175 180 184
## 23 7 3 1 1 11 1 44 1 1 9 3 1 3 8 1
## 185 186 190 195 200 210 215 220 225 230 235 240 250 260 265 270
## 2 1 2 2 69 2 1 2 1 3 3 3 12 1 1 2
## 275 300 320 350 360 380 400 415 420 430 490 500 525 544 or more <NA>
## 1 31 2 3 1 1 10 1 1 1 1 3 1 9 679
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q18)[na.exclude(mydata$eh_s10q18)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q18", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q18. Q727: In the last 7 days how much did the household spend on Sugar, Jam, honey,
## -998 0 3 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
## 1 34 1 10 2 2 1 1 26 13 17 28 44 65 24 8 18 4 94 10 28 13 53 39 29 3 32 3 114 1 10 4 13 25 21 5 15 15
## 40 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 70 71 72 73 74 75 76 77 78 80
## 108 40 4 21 47 12 5 52 1 130 3 18 3 7 9 15 3 10 3 69 2 3 4 3 4 9 4 5 24 1 7 2 2 11 2 5 3 34
## 82 83 84 86 87 88 90 91 92 94 95 96 98 100 104 105 106 108 110 111 112 113 115 116 119 120 121 125 126 127 128 130 132 133 135 136 138 139
## 1 1 19 2 1 10 14 15 3 4 2 6 12 93 3 24 1 2 8 1 7 1 1 1 1 20 1 1 8 2 1 6 2 1 3 1 1 1
## 140 141 144 145 147 150 154 158 159 160 161 164 167 168 170 172 174 175 180 182 185 188 190 196 200 210 218 220 224 230 231 238 240 241 250 272 278 280
## 7 1 2 2 1 29 5 3 2 2 1 2 1 3 1 3 1 1 4 3 1 2 1 1 35 2 1 2 1 2 1 1 1 1 8 1 2 2
## 285 300 342 350 380 383 392 393 400 420 450 461 480 500 530 550 562 590 600 700 800 1000 2800 <NA>
## 1 13 1 1 1 1 1 1 4 2 1 1 2 4 1 1 1 1 1 1 2 1 1 313
## [1] "Frequency table after encoding"
## eh_s10q18. Q727: In the last 7 days how much did the household spend on Sugar, Jam, honey,
## -998 0 3 5 6 7 8 9 10 11 12 13 14 15 16 17
## 1 34 1 10 2 2 1 1 26 13 17 28 44 65 24 8
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
## 18 4 94 10 28 13 53 39 29 3 32 3 114 1 10 4
## 34 35 36 37 38 39 40 42 43 44 45 46 47 48 49 50
## 13 25 21 5 15 15 108 40 4 21 47 12 5 52 1 130
## 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
## 3 18 3 7 9 15 3 10 3 69 2 3 4 3 4 9
## 67 68 70 71 72 73 74 75 76 77 78 80 82 83 84 86
## 4 5 24 1 7 2 2 11 2 5 3 34 1 1 19 2
## 87 88 90 91 92 94 95 96 98 100 104 105 106 108 110 111
## 1 10 14 15 3 4 2 6 12 93 3 24 1 2 8 1
## 112 113 115 116 119 120 121 125 126 127 128 130 132 133 135 136
## 7 1 1 1 1 20 1 1 8 2 1 6 2 1 3 1
## 138 139 140 141 144 145 147 150 154 158 159 160 161 164 167 168
## 1 1 7 1 2 2 1 29 5 3 2 2 1 2 1 3
## 170 172 174 175 180 182 185 188 190 196 200 210 218 220 224 230
## 1 3 1 1 4 3 1 2 1 1 35 2 1 2 1 2
## 231 238 240 241 250 272 278 280 285 300 342 350 380 383 392 393
## 1 1 1 1 8 1 2 2 1 13 1 1 1 1 1 1
## 400 420 450 461 480 500 503 or more <NA>
## 4 2 1 1 2 4 10 313
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q20)[na.exclude(mydata$eh_s10q20)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q20", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q20. Q729: In the last 7 days how much did the household spend on Non-alcoholic drink
## -998 0 6 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
## 1 22 1 2 8 2 6 1 9 5 3 3 24 11 101 15 28 16 19 50 1 3 8 3 33 1 4 8 4 13 31 3 18 2 67 3 20 2
## 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81
## 12 9 18 5 16 11 102 2 7 6 5 14 11 3 6 3 55 1 4 8 6 13 4 2 5 9 58 3 9 4 8 22 6 35 10 1 28 8
## 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 112 113 115 116 117 119 120 121 122
## 7 2 48 12 6 3 4 2 19 1 5 2 8 8 5 7 11 6 133 4 4 1 2 7 2 3 3 3 11 4 3 4 4 2 2 24 2 5
## 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 147 148 150 151 152 153 154 155 156 158 159 160 162 163 164
## 2 8 10 4 1 4 2 4 2 3 4 7 3 5 2 1 1 31 1 3 2 4 1 6 3 68 5 4 1 20 1 3 5 3 9 2 3 1
## 165 166 167 168 169 170 171 172 173 174 175 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 195 196 197 198 200 203 204 205 206 208
## 5 4 2 27 1 8 3 3 1 3 4 1 3 3 13 1 4 1 4 4 5 1 2 4 4 4 2 3 3 1 1 5 74 2 3 1 1 3
## 210 213 214 215 216 217 218 220 221 222 224 225 227 230 231 232 234 235 236 238 240 241 242 243 247 249 250 252 254 255 256 258 259 260 264 265 267 268
## 12 2 1 5 1 3 2 5 2 2 3 1 2 6 6 1 1 1 1 6 3 1 1 1 1 1 7 7 1 2 2 1 1 4 2 2 1 2
## 270 271 273 274 275 277 279 280 282 284 286 287 290 292 293 294 300 304 305 307 308 311 313 314 315 320 321 322 324 325 330 334 336 337 343 345 347 350
## 1 1 2 1 2 1 1 6 1 1 2 2 4 1 1 3 33 1 1 1 5 1 1 2 1 6 1 1 1 1 2 1 3 1 3 1 1 7
## 351 353 354 356 360 362 363 366 368 373 380 385 388 390 392 395 400 401 405 409 416 420 425 440 447 448 450 455 456 476 500 504 508 512 520 524 528 539
## 1 1 1 1 3 1 1 1 1 1 2 1 1 2 1 1 12 1 1 1 1 2 1 1 1 1 4 1 1 1 16 1 2 1 1 1 1 1
## 544 551 560 580 610 630 652 677 700 718 790 800 840 930 1092 1400 1500 4278 <NA>
## 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 75
## [1] "Frequency table after encoding"
## eh_s10q20. Q729: In the last 7 days how much did the household spend on Non-alcoholic drink
## -998 0 6 9 10 11 12 13 14 15 16 17 18 19 20 21
## 1 22 1 2 8 2 6 1 9 5 3 3 24 11 101 15
## 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
## 28 16 19 50 1 3 8 3 33 1 4 8 4 13 31 3
## 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
## 18 2 67 3 20 2 12 9 18 5 16 11 102 2 7 6
## 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69
## 5 14 11 3 6 3 55 1 4 8 6 13 4 2 5 9
## 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
## 58 3 9 4 8 22 6 35 10 1 28 8 7 2 48 12
## 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
## 6 3 4 2 19 1 5 2 8 8 5 7 11 6 133 4
## 102 103 104 105 106 107 108 109 110 112 113 115 116 117 119 120
## 4 1 2 7 2 3 3 3 11 4 3 4 4 2 2 24
## 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136
## 2 5 2 8 10 4 1 4 2 4 2 3 4 7 3 5
## 137 138 139 140 141 142 143 144 145 147 148 150 151 152 153 154
## 2 1 1 31 1 3 2 4 1 6 3 68 5 4 1 20
## 155 156 158 159 160 162 163 164 165 166 167 168 169 170 171 172
## 1 3 5 3 9 2 3 1 5 4 2 27 1 8 3 3
## 173 174 175 177 178 179 180 181 182 183 184 185 186 187 188 189
## 1 3 4 1 3 3 13 1 4 1 4 4 5 1 2 4
## 190 191 192 193 195 196 197 198 200 203 204 205 206 208 210 213
## 4 4 2 3 3 1 1 5 74 2 3 1 1 3 12 2
## 214 215 216 217 218 220 221 222 224 225 227 230 231 232 234 235
## 1 5 1 3 2 5 2 2 3 1 2 6 6 1 1 1
## 236 238 240 241 242 243 247 249 250 252 254 255 256 258 259 260
## 1 6 3 1 1 1 1 1 7 7 1 2 2 1 1 4
## 264 265 267 268 270 271 273 274 275 277 279 280 282 284 286 287
## 2 2 1 2 1 1 2 1 2 1 1 6 1 1 2 2
## 290 292 293 294 300 304 305 307 308 311 313 314 315 320 321 322
## 4 1 1 3 33 1 1 1 5 1 1 2 1 6 1 1
## 324 325 330 334 336 337 343 345 347 350 351 353 354 356 360 362
## 1 1 2 1 3 1 3 1 1 7 1 1 1 1 3 1
## 363 366 368 373 380 385 388 390 392 395 400 401 405 409 416 420
## 1 1 1 1 2 1 1 2 1 1 12 1 1 1 1 2
## 425 440 447 448 450 455 456 476 500 504 508 512 520 524 528 539
## 1 1 1 1 4 1 1 1 16 1 2 1 1 1 1 1
## 544 551 560 580 610 630 652 677 698 or more <NA>
## 1 1 1 1 1 1 1 1 12 75
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q22)[na.exclude(mydata$eh_s10q22)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q22", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q22. Q731: In the last 7 days how much did the household spend on Alcoholic drinks?
## 0 1 15 20 22 24 30 32 33 34 35 36 39 40 42 43 44 45 46 47 48 49 50 52 53 55 58 60 64 65 66 70 75 80 81 84 85 87
## 99 1 1 9 1 1 4 1 1 1 2 1 1 31 7 4 3 43 5 9 4 1 56 2 1 4 1 7 1 1 1 4 4 10 1 4 2 1
## 90 92 94 95 96 100 105 110 120 125 129 130 132 135 136 138 140 145 150 156 160 168 170 172 175 180 190 200 210 220 225 228 240 250 255 260 270 280
## 32 2 1 5 2 77 1 1 24 2 1 2 2 9 1 1 5 1 24 1 3 1 3 1 2 9 2 47 1 2 4 1 3 7 1 2 2 4
## 285 294 300 303 315 336 350 368 375 385 400 450 470 500 516 560 570 600 700 800 900 1000 1200 1260 1400 1500 2000 2500 3000 4100 <NA>
## 1 2 7 1 3 1 4 1 1 2 4 3 1 9 1 1 1 3 1 2 1 2 1 1 1 3 3 1 1 1 1605
## [1] "Frequency table after encoding"
## eh_s10q22. Q731: In the last 7 days how much did the household spend on Alcoholic drinks?
## 0 1 15 20 22 24 30 32 33 34 35 36 39 40
## 99 1 1 9 1 1 4 1 1 1 2 1 1 31
## 42 43 44 45 46 47 48 49 50 52 53 55 58 60
## 7 4 3 43 5 9 4 1 56 2 1 4 1 7
## 64 65 66 70 75 80 81 84 85 87 90 92 94 95
## 1 1 1 4 4 10 1 4 2 1 32 2 1 5
## 96 100 105 110 120 125 129 130 132 135 136 138 140 145
## 2 77 1 1 24 2 1 2 2 9 1 1 5 1
## 150 156 160 168 170 172 175 180 190 200 210 220 225 228
## 24 1 3 1 3 1 2 9 2 47 1 2 4 1
## 240 250 255 260 270 280 285 294 300 303 315 336 350 368
## 3 7 1 2 2 4 1 2 7 1 3 1 4 1
## 375 385 400 450 470 500 516 560 570 600 700 800 900 1000
## 1 2 4 3 1 9 1 1 1 3 1 2 1 2
## 1200 1260 1400 1500 2000 or more <NA>
## 1 1 1 3 6 1605
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q24)[na.exclude(mydata$eh_s10q24)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q24", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q24. Q733: In the last 7 days how much did the household spend on Tobacco? Cigarette
## -998 0 3 6 8 9 10 14 15 17 18 20 21 24 25 26 27 28 30 32 35 36 37 40 42 45 48 49 50 51 52 54 55 56 58 60 63 65
## 2 9 2 1 3 2 10 3 6 1 2 32 8 2 4 1 2 9 40 1 6 1 1 17 12 5 2 1 48 2 5 2 7 5 1 62 23 2
## 68 70 74 75 77 80 82 84 90 100 102 104 105 106 107 108 110 112 120 125 126 130 138 140 144 147 150 152 156 160 165 168 170 174 175 180 189 192
## 1 26 1 4 1 15 1 16 8 55 1 1 37 1 1 1 4 2 33 3 4 1 2 48 1 3 30 1 2 6 2 2 1 1 15 18 1 1
## 195 196 200 203 210 216 220 221 224 227 234 240 245 250 252 255 273 275 280 300 315 329 340 350 356 357 360 378 385 400 406 420 450 455 480 490 500 540
## 1 3 23 1 66 1 1 1 1 1 1 13 2 6 1 1 1 2 16 27 3 1 1 32 1 2 2 1 10 4 2 52 3 4 1 4 7 1
## 560 576 577 588 595 600 630 700 770 805 840 900 910 940 1050 1092 1200 1400 1418 1750 2205 <NA>
## 10 1 1 1 1 3 3 11 1 1 2 1 1 1 1 1 2 1 1 1 1 1249
## [1] "Frequency table after encoding"
## eh_s10q24. Q733: In the last 7 days how much did the household spend on Tobacco? Cigarette
## -998 0 3 6 8 9 10 14 15 17 18 20 21 24
## 2 9 2 1 3 2 10 3 6 1 2 32 8 2
## 25 26 27 28 30 32 35 36 37 40 42 45 48 49
## 4 1 2 9 40 1 6 1 1 17 12 5 2 1
## 50 51 52 54 55 56 58 60 63 65 68 70 74 75
## 48 2 5 2 7 5 1 62 23 2 1 26 1 4
## 77 80 82 84 90 100 102 104 105 106 107 108 110 112
## 1 15 1 16 8 55 1 1 37 1 1 1 4 2
## 120 125 126 130 138 140 144 147 150 152 156 160 165 168
## 33 3 4 1 2 48 1 3 30 1 2 6 2 2
## 170 174 175 180 189 192 195 196 200 203 210 216 220 221
## 1 1 15 18 1 1 1 3 23 1 66 1 1 1
## 224 227 234 240 245 250 252 255 273 275 280 300 315 329
## 1 1 1 13 2 6 1 1 1 2 16 27 3 1
## 340 350 356 357 360 378 385 400 406 420 450 455 480 490
## 1 32 1 2 2 1 10 4 2 52 3 4 1 4
## 500 540 560 576 577 588 595 600 630 700 770 805 840 900
## 7 1 10 1 1 1 1 3 3 11 1 1 2 1
## 910 940 1050 1092 1179 or more <NA>
## 1 1 1 1 6 1249
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q26)[na.exclude(mydata$eh_s10q26)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q26", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q26. Q735: In the last 7 days how much did the household spend on Spices and condimen
## -998 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 1 40 2 4 1 3 8 3 3 3 3 27 2 12 5 7 22 8 5 2 2 135 8 7 10 2 43 12 4 12 3 120 7 6 7 6 21 13
## 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74
## 6 4 5 41 4 7 9 6 18 7 2 10 7 433 7 6 6 4 9 9 7 6 8 46 5 3 7 3 5 6 5 7 3 49 1 1 2 3
## 75 76 77 78 79 80 81 82 83 84 85 86 87 88 90 91 92 93 94 95 96 98 99 100 102 103 104 105 106 107 108 109 110 111 112 115 117 118
## 14 3 5 4 1 30 3 3 1 2 3 3 2 5 9 2 2 1 1 4 1 2 3 419 2 1 1 2 3 1 2 1 2 2 2 1 3 3
## 119 120 124 125 126 129 130 133 138 140 143 147 148 150 153 154 166 169 170 175 178 180 200 210 212 215 248 250 265 270 280 300 350 355 400 430 500 600
## 1 10 1 4 1 1 2 1 1 10 1 1 1 84 1 1 2 1 1 2 1 5 122 7 1 1 1 12 2 1 1 52 3 1 8 1 12 1
## 700 900 1200 <NA>
## 3 1 1 29
## [1] "Frequency table after encoding"
## eh_s10q26. Q735: In the last 7 days how much did the household spend on Spices and condimen
## -998 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 1 40 2 4 1 3 8 3 3 3 3 27 2 12 5 7
## 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
## 22 8 5 2 2 135 8 7 10 2 43 12 4 12 3 120
## 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
## 7 6 7 6 21 13 6 4 5 41 4 7 9 6 18 7
## 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62
## 2 10 7 433 7 6 6 4 9 9 7 6 8 46 5 3
## 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78
## 7 3 5 6 5 7 3 49 1 1 2 3 14 3 5 4
## 79 80 81 82 83 84 85 86 87 88 90 91 92 93 94 95
## 1 30 3 3 1 2 3 3 2 5 9 2 2 1 1 4
## 96 98 99 100 102 103 104 105 106 107 108 109 110 111 112 115
## 1 2 3 419 2 1 1 2 3 1 2 1 2 2 2 1
## 117 118 119 120 124 125 126 129 130 133 138 140 143 147 148 150
## 3 3 1 10 1 4 1 1 2 1 1 10 1 1 1 84
## 153 154 166 169 170 175 178 180 200 210 212 215 248 250 265 270
## 1 1 2 1 1 2 1 5 122 7 1 1 1 12 2 1
## 280 300 350 355 400 430 500 or more <NA>
## 1 52 3 1 8 1 18 29
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q28)[na.exclude(mydata$eh_s10q28)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q28", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q28. Q737: In the last 7 days how much did the household spend on Prepared foods ? V
## 0 5 10 12 15 18 20 25 30 32 35 37 40 45 48 50 55 60 65 70 72 75 80 90 100 105 108 110 112 120 125 130 135 140 150 155 156 160
## 13 1 10 1 9 1 44 11 50 1 14 2 44 13 1 87 4 53 1 36 1 10 24 25 108 6 1 6 1 22 1 5 2 22 53 1 1 7
## 168 170 175 180 190 198 199 200 210 220 222 225 229 240 245 250 255 260 270 280 300 315 320 340 350 355 360 390 400 420 450 490 500 502 540 560 600 700
## 1 3 2 5 2 1 1 54 9 2 1 1 1 2 5 15 1 1 3 13 36 2 1 1 15 1 1 1 8 11 1 3 11 1 1 4 4 4
## 840 1000 1050 1055 1200 1400 2075 2730 <NA>
## 1 3 2 1 1 1 1 1 1356
## [1] "Frequency table after encoding"
## eh_s10q28. Q737: In the last 7 days how much did the household spend on Prepared foods ? V
## 0 5 10 12 15 18 20 25 30 32 35 37 40 45
## 13 1 10 1 9 1 44 11 50 1 14 2 44 13
## 48 50 55 60 65 70 72 75 80 90 100 105 108 110
## 1 87 4 53 1 36 1 10 24 25 108 6 1 6
## 112 120 125 130 135 140 150 155 156 160 168 170 175 180
## 1 22 1 5 2 22 53 1 1 7 1 3 2 5
## 190 198 199 200 210 220 222 225 229 240 245 250 255 260
## 2 1 1 54 9 2 1 1 1 2 5 15 1 1
## 270 280 300 315 320 340 350 355 360 390 400 420 450 490
## 3 13 36 2 1 1 15 1 1 1 8 11 1 3
## 500 502 540 560 600 700 840 1000 1050 1051 or more <NA>
## 11 1 1 4 4 4 1 3 2 5 1356
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q30)[na.exclude(mydata$eh_s10q30)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q30", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q30. Q739: In the last 7 days how much did the household spend on other food items?
## -999 -998 0 3 5 7 8 12 14 20 28 30 32 40 46 50 60 65 70 72 75 80 89 90 99 100 120 130 140 150 160 175 182 200 240 250 280 300
## 8 67 2105 2 2 1 1 1 1 5 1 5 1 4 1 9 3 1 3 1 1 3 1 1 1 9 1 1 2 2 1 1 1 2 1 1 2 3
## 350 355 390 400 500 550 600 700 998 1015 1500 2000
## 2 1 1 1 16 1 2 2 1 1 1 3
## [1] "Frequency table after encoding"
## eh_s10q30. Q739: In the last 7 days how much did the household spend on other food items?
## -999 -998 0 3 5 7 8 12 14 20 28 30 32 40 46 50
## 8 67 2105 2 2 1 1 1 1 5 1 5 1 4 1 9
## 60 65 70 72 75 80 89 90 99 100 120 130 140 150 160 175
## 3 1 3 1 1 3 1 1 1 9 1 1 2 2 1 1
## 182 200 240 250 280 300 350 355 390 400 500 or more
## 1 2 1 1 2 3 2 1 1 1 27
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q31)[na.exclude(mydata$eh_s10q31)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q31", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q31. Q741: In the last 30 days how much did the household spend on Airtime, internet,
## -999 -998 0 10 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 28 29 30 32 34 35 36 38 39 40 41 42 44 45 46 48 50 51 52
## 1 38 341 11 8 9 1 35 1 11 8 1 47 1 14 12 10 8 7 1 1 83 4 11 2 28 1 6 43 1 2 10 12 12 7 107 5 4
## 54 56 60 63 64 65 66 68 69 70 72 75 80 84 88 90 92 96 98 100 102 104 105 108 110 114 115 118 120 124 125 126 130 132 135 136 140 144
## 14 2 82 1 2 1 2 11 4 7 29 6 56 2 6 6 12 4 1 268 1 3 2 5 5 1 1 1 54 1 1 1 1 1 2 3 8 13
## 150 153 155 156 160 170 172 176 180 184 190 194 195 196 198 200 207 208 210 212 215 216 220 224 225 230 231 240 250 252 255 260 264 270 276 280 283 288
## 98 2 1 1 27 1 1 6 19 8 1 1 2 1 1 135 1 1 2 1 2 9 5 1 3 4 1 29 18 1 1 2 3 4 2 7 1 4
## 291 292 293 300 320 330 340 350 352 358 360 375 380 396 400 408 420 430 440 450 460 476 480 485 500 512 520 540 550 552 560 576 580 600 620 624 625 660
## 1 1 1 107 7 5 5 5 1 1 2 2 1 1 38 3 4 2 1 9 1 1 5 1 55 1 2 1 2 2 1 1 1 10 2 1 1 2
## 663 670 700 720 750 800 820 840 900 910 924 1000 1014 1040 1144 1200 1299 1320 1350 1480 1500 1632 1650 1728 1799 1800 1920 2000 2049 2200 2300 2350 2400 7200 <NA>
## 1 1 5 2 1 8 2 1 2 1 1 10 1 1 1 8 1 1 1 1 3 1 1 1 1 2 1 3 1 1 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q31. Q741: In the last 30 days how much did the household spend on Airtime, internet,
## -999 -998 0 10 12 13 14 15 16 17 18 19 20 21
## 1 38 341 11 8 9 1 35 1 11 8 1 47 1
## 22 23 24 25 26 28 29 30 32 34 35 36 38 39
## 14 12 10 8 7 1 1 83 4 11 2 28 1 6
## 40 41 42 44 45 46 48 50 51 52 54 56 60 63
## 43 1 2 10 12 12 7 107 5 4 14 2 82 1
## 64 65 66 68 69 70 72 75 80 84 88 90 92 96
## 2 1 2 11 4 7 29 6 56 2 6 6 12 4
## 98 100 102 104 105 108 110 114 115 118 120 124 125 126
## 1 268 1 3 2 5 5 1 1 1 54 1 1 1
## 130 132 135 136 140 144 150 153 155 156 160 170 172 176
## 1 1 2 3 8 13 98 2 1 1 27 1 1 6
## 180 184 190 194 195 196 198 200 207 208 210 212 215 216
## 19 8 1 1 2 1 1 135 1 1 2 1 2 9
## 220 224 225 230 231 240 250 252 255 260 264 270 276 280
## 5 1 3 4 1 29 18 1 1 2 3 4 2 7
## 283 288 291 292 293 300 320 330 340 350 352 358 360 375
## 1 4 1 1 1 107 7 5 5 5 1 1 2 2
## 380 396 400 408 420 430 440 450 460 476 480 485 500 512
## 1 1 38 3 4 2 1 9 1 1 5 1 55 1
## 520 540 550 552 560 576 580 600 620 624 625 660 663 670
## 2 1 2 2 1 1 1 10 2 1 1 2 1 1
## 700 720 750 800 820 840 900 910 924 1000 1014 1040 1144 1200
## 5 2 1 8 2 1 2 1 1 10 1 1 1 8
## 1299 1320 1350 1480 1500 1632 1650 1728 1799 or more <NA>
## 1 1 1 1 3 1 1 1 13 1
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q32)[na.exclude(mydata$eh_s10q32)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q32", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q32. Q743: In the last 30 days how much did the household spend on Travel, transport,
## -999 -998 0 1 10 14 15 20 22 24 26 30 32 34 35 40 42 45 46 48 50 52 56 60 64 66 68 70 72 80 84 88
## 2 3 526 4 4 2 1 17 1 1 2 6 4 2 1 22 3 4 1 3 25 2 2 18 3 1 1 8 1 35 1 1
## 90 96 100 104 105 108 112 116 120 125 128 130 136 140 144 148 150 154 160 170 175 176 178 180 182 184 190 192 200 212 215 216
## 5 2 130 2 1 2 1 1 31 3 1 2 2 12 2 1 35 1 18 2 1 1 1 9 1 2 3 2 160 1 1 2
## 220 225 228 235 240 250 252 260 270 272 280 290 297 300 320 325 328 330 336 340 344 348 350 360 368 376 378 380 382 384 400 408
## 5 1 1 1 22 20 1 2 2 1 10 3 1 112 13 1 1 1 1 4 1 1 8 5 1 1 2 2 1 2 72 2
## 420 426 428 440 445 448 450 460 464 472 477 480 490 492 496 500 510 520 522 530 540 550 560 564 572 576 580 584 600 620 628 640
## 5 1 1 5 1 1 6 1 2 1 1 19 2 1 1 111 3 4 1 1 2 1 5 1 1 1 1 2 55 2 1 9
## 650 660 672 680 700 702 720 728 730 740 750 760 770 800 810 820 840 856 860 864 880 896 900 912 920 932 936 940 960 975 1000 1002
## 5 1 1 8 22 1 10 1 1 2 6 4 1 45 1 2 5 1 1 1 2 1 16 1 2 1 1 1 7 1 68 1
## 1030 1040 1044 1050 1064 1080 1100 1120 1130 1150 1160 1200 1240 1248 1256 1280 1300 1320 1350 1360 1380 1400 1420 1430 1440 1500 1520 1530 1550 1560 1570 1600
## 1 2 1 2 1 3 5 3 1 1 1 42 2 1 1 3 4 4 1 1 1 6 1 1 6 39 1 1 1 1 1 11
## 1624 1650 1658 1664 1680 1700 1710 1720 1724 1750 1760 1800 1840 1860 1910 1920 1968 1975 2000 2050 2100 2110 2140 2144 2160 2200 2220 2240 2250 2280 2320 2328
## 2 1 1 1 8 2 1 1 1 2 2 11 1 1 1 6 1 1 21 2 3 1 1 1 1 1 1 4 1 1 1 1
## 2360 2400 2408 2470 2500 2560 2600 2640 2672 2700 2800 2816 2832 2880 2900 3000 3026 3080 3120 3200 3250 3280 3400 3480 3500 3600 3660 3700 3750 3800 3820 3840
## 2 11 1 1 2 1 6 1 1 2 3 1 1 2 1 28 1 1 3 1 1 1 2 1 4 7 1 1 1 1 1 1
## 3880 4000 4050 4080 4200 4416 4500 4600 4768 4800 4950 5000 5184 5400 5496 5700 6000 6100 7000 7200 7520 8000 8400 9200 10616 10620 11760 15808 20000
## 1 9 1 1 2 1 2 1 1 1 1 3 1 1 1 2 5 1 1 1 1 1 1 1 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q32. Q743: In the last 30 days how much did the household spend on Travel, transport,
## -999 -998 0 1 10 14 15 20 22 24 26 30 32 34
## 2 3 526 4 4 2 1 17 1 1 2 6 4 2
## 35 40 42 45 46 48 50 52 56 60 64 66 68 70
## 1 22 3 4 1 3 25 2 2 18 3 1 1 8
## 72 80 84 88 90 96 100 104 105 108 112 116 120 125
## 1 35 1 1 5 2 130 2 1 2 1 1 31 3
## 128 130 136 140 144 148 150 154 160 170 175 176 178 180
## 1 2 2 12 2 1 35 1 18 2 1 1 1 9
## 182 184 190 192 200 212 215 216 220 225 228 235 240 250
## 1 2 3 2 160 1 1 2 5 1 1 1 22 20
## 252 260 270 272 280 290 297 300 320 325 328 330 336 340
## 1 2 2 1 10 3 1 112 13 1 1 1 1 4
## 344 348 350 360 368 376 378 380 382 384 400 408 420 426
## 1 1 8 5 1 1 2 2 1 2 72 2 5 1
## 428 440 445 448 450 460 464 472 477 480 490 492 496 500
## 1 5 1 1 6 1 2 1 1 19 2 1 1 111
## 510 520 522 530 540 550 560 564 572 576 580 584 600 620
## 3 4 1 1 2 1 5 1 1 1 1 2 55 2
## 628 640 650 660 672 680 700 702 720 728 730 740 750 760
## 1 9 5 1 1 8 22 1 10 1 1 2 6 4
## 770 800 810 820 840 856 860 864 880 896 900 912 920 932
## 1 45 1 2 5 1 1 1 2 1 16 1 2 1
## 936 940 960 975 1000 1002 1030 1040 1044 1050 1064 1080 1100 1120
## 1 1 7 1 68 1 1 2 1 2 1 3 5 3
## 1130 1150 1160 1200 1240 1248 1256 1280 1300 1320 1350 1360 1380 1400
## 1 1 1 42 2 1 1 3 4 4 1 1 1 6
## 1420 1430 1440 1500 1520 1530 1550 1560 1570 1600 1624 1650 1658 1664
## 1 1 6 39 1 1 1 1 1 11 2 1 1 1
## 1680 1700 1710 1720 1724 1750 1760 1800 1840 1860 1910 1920 1968 1975
## 8 2 1 1 1 2 2 11 1 1 1 6 1 1
## 2000 2050 2100 2110 2140 2144 2160 2200 2220 2240 2250 2280 2320 2328
## 21 2 3 1 1 1 1 1 1 4 1 1 1 1
## 2360 2400 2408 2470 2500 2560 2600 2640 2672 2700 2800 2816 2832 2880
## 2 11 1 1 2 1 6 1 1 2 3 1 1 2
## 2900 3000 3026 3080 3120 3200 3250 3280 3400 3480 3500 3600 3660 3700
## 1 28 1 1 3 1 1 1 2 1 4 7 1 1
## 3750 3800 3820 3840 3880 4000 4050 4080 4200 4416 4500 4600 4768 4800
## 1 1 1 1 1 9 1 1 2 1 2 1 1 1
## 4950 5000 5184 5400 5496 5700 6000 6056 or more
## 1 3 1 1 1 2 5 12
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q33)[na.exclude(mydata$eh_s10q33)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q33", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q33. Q745: In the last 30 days how much did the household spend on Lottery tickets/ga
## -999 -998 0 3 5 10 12 15 20 21 25 30 40 45 50 60 70 80 90 100 105 120 140 150 160 180 200 210 220 240 250 270 280 300 396 400 450 500
## 6 2 2052 1 4 8 1 6 25 1 4 10 17 1 6 3 2 14 1 21 2 4 2 9 4 1 14 1 1 5 1 1 1 14 1 4 2 7
## 600 840 900 1000 1200 1400 1500 1680 1800 2700 2900 4200 5400 <NA>
## 7 1 4 1 3 1 1 1 4 1 1 1 1 2
## [1] "Frequency table after encoding"
## eh_s10q33. Q745: In the last 30 days how much did the household spend on Lottery tickets/ga
## -999 -998 0 3 5 10 12 15 20 21 25 30 40 45
## 6 2 2052 1 4 8 1 6 25 1 4 10 17 1
## 50 60 70 80 90 100 105 120 140 150 160 180 200 210
## 6 3 2 14 1 21 2 4 2 9 4 1 14 1
## 220 240 250 270 280 300 396 400 450 500 600 840 900 1000
## 1 5 1 1 1 14 1 4 2 7 7 1 4 1
## 1200 or more <NA>
## 14 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q34)[na.exclude(mydata$eh_s10q34)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q34", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q34. Q747: In the last 30 days how much did the household spend on Clothing and shoes
## -999 -998 0 18 20 30 35 50 60 65 70 75 80 88 90 95 100 105 115 118 120 125 130 150 170 175 180 195 199 200 209 215
## 2 1 1552 1 1 1 1 3 4 1 3 1 5 2 2 1 26 1 1 1 10 1 1 19 1 1 8 1 1 42 1 1
## 225 230 235 240 250 259 260 263 270 275 280 285 290 292 298 300 315 320 330 340 350 358 370 375 380 390 399 400 420 430 435 440
## 1 1 1 1 23 1 2 1 1 1 2 1 1 1 1 33 1 1 1 2 26 1 4 1 2 1 2 16 4 1 1 2
## 445 450 455 459 465 470 480 500 510 520 525 530 538 540 550 560 580 600 610 625 630 650 670 680 700 712 720 730 750 760 780 790
## 1 12 1 1 1 1 4 73 1 1 2 1 1 2 8 1 1 29 1 1 3 12 3 3 18 1 2 1 5 1 2 1
## 800 810 815 830 840 850 880 885 900 920 926 950 960 975 980 1000 1050 1070 1080 1100 1130 1148 1150 1200 1230 1280 1300 1354 1395 1400 1450 1500
## 10 2 1 1 2 2 2 2 8 1 1 1 4 1 3 73 2 1 1 1 1 1 1 9 1 1 6 1 1 3 1 34
## 1600 1700 1800 1900 1910 1950 2000 2080 2300 2500 2550 2555 2600 2700 2720 2900 3000 3300 3500 3900 3980 4000 4300 4500 5000 5200 6700 13000 20000 <NA>
## 2 1 1 1 1 1 20 1 1 10 1 1 1 1 1 2 9 1 3 1 1 7 1 1 4 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q34. Q747: In the last 30 days how much did the household spend on Clothing and shoes
## -999 -998 0 18 20 30 35 50 60 65 70 75 80 88
## 2 1 1552 1 1 1 1 3 4 1 3 1 5 2
## 90 95 100 105 115 118 120 125 130 150 170 175 180 195
## 2 1 26 1 1 1 10 1 1 19 1 1 8 1
## 199 200 209 215 225 230 235 240 250 259 260 263 270 275
## 1 42 1 1 1 1 1 1 23 1 2 1 1 1
## 280 285 290 292 298 300 315 320 330 340 350 358 370 375
## 2 1 1 1 1 33 1 1 1 2 26 1 4 1
## 380 390 399 400 420 430 435 440 445 450 455 459 465 470
## 2 1 2 16 4 1 1 2 1 12 1 1 1 1
## 480 500 510 520 525 530 538 540 550 560 580 600 610 625
## 4 73 1 1 2 1 1 2 8 1 1 29 1 1
## 630 650 670 680 700 712 720 730 750 760 780 790 800 810
## 3 12 3 3 18 1 2 1 5 1 2 1 10 2
## 815 830 840 850 880 885 900 920 926 950 960 975 980 1000
## 1 1 2 2 2 2 8 1 1 1 4 1 3 73
## 1050 1070 1080 1100 1130 1148 1150 1200 1230 1280 1300 1354 1395 1400
## 2 1 1 1 1 1 1 9 1 1 6 1 1 3
## 1450 1500 1600 1700 1800 1900 1910 1950 2000 2080 2300 2500 2550 2555
## 1 34 2 1 1 1 1 1 20 1 1 10 1 1
## 2600 2700 2720 2900 3000 3300 3500 3900 3980 4000 or more <NA>
## 1 1 1 2 9 1 3 1 1 17 1
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q35)[na.exclude(mydata$eh_s10q35)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q35", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q35. Q749: In the last 30 days how much did the household spend on Recreation/enterta
## -999 0 20 30 35 50 90 100 150 175 200 235 250 300 350 500 600 700 800 900 1000 1050 1200 1500 2000 2500 3000 4000 <NA>
## 4 2206 1 2 1 2 1 4 1 1 10 1 1 7 1 14 6 2 1 2 9 1 1 1 2 1 1 2 2
## [1] "Frequency table after encoding"
## eh_s10q35. Q749: In the last 30 days how much did the household spend on Recreation/enterta
## -999 0 20 30 35 50 90 100 150 175 200 235 250 300
## 4 2206 1 2 1 2 1 4 1 1 10 1 1 7
## 350 500 600 700 800 900 1000 or more <NA>
## 1 14 6 2 1 2 18 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q36)[na.exclude(mydata$eh_s10q36)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q36", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q36. Q751: In the last 30 days how much did the household spend on Personal items? H
## -998 0 1 7 8 10 12 14 15 16 18 19 20 21 22 24 25 27 28 30 31 32 34 35 36 37 38 39 40 42 43 44 45 46 47 48 49 50
## 3 237 4 1 5 1 4 4 5 4 1 1 15 2 2 8 15 3 12 17 1 16 5 10 10 1 1 3 24 3 1 3 13 1 3 12 3 58
## 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 84 85 86 87 88 89 90
## 5 3 1 6 13 4 4 2 25 1 2 1 13 8 4 3 3 1 22 1 11 1 3 7 3 2 2 4 27 1 1 15 2 3 1 3 1 12
## 91 92 93 94 95 96 98 99 100 102 103 104 105 106 107 108 109 110 112 113 114 115 116 117 118 119 120 121 122 124 125 126 127 128 129 130 131 132
## 2 6 2 4 5 17 3 8 142 1 1 3 5 2 1 4 6 6 4 1 3 5 1 1 3 8 36 2 1 2 5 3 3 4 1 13 2 2
## 133 135 136 137 138 139 140 141 142 143 144 145 147 148 149 150 151 152 154 155 156 158 159 160 163 164 165 169 170 171 172 173 174 175 178 179 180 181
## 2 6 3 2 2 1 15 3 2 1 3 10 2 1 3 86 3 3 4 3 1 1 2 15 1 2 3 2 3 1 2 2 3 2 3 5 11 2
## 182 184 186 188 189 190 192 193 194 195 196 197 198 199 200 204 206 207 208 209 210 212 213 214 215 216 217 218 219 220 222 223 225 227 228 229 230 231
## 3 3 1 3 3 6 4 1 1 1 5 4 3 6 167 3 1 2 3 3 9 2 2 2 5 2 1 3 3 6 1 1 2 3 2 1 8 1
## 232 233 234 235 236 237 238 239 240 241 244 246 248 250 251 252 253 255 256 257 259 260 262 263 264 265 267 268 269 270 271 273 275 276 277 278 280 282
## 5 1 4 1 3 3 4 2 7 1 3 4 1 40 2 1 1 2 3 1 4 4 2 1 4 2 2 1 4 4 1 1 1 6 1 2 8 1
## 283 284 285 286 287 288 289 290 292 293 294 298 299 300 301 302 305 308 309 310 312 313 314 315 316 320 321 322 323 325 326 328 330 331 336 339 340 342
## 1 1 1 1 1 2 3 4 3 1 1 1 3 148 1 2 1 3 2 2 1 1 2 3 1 6 1 2 2 2 1 3 1 2 2 1 3 1
## 345 349 350 354 356 358 359 360 364 367 370 372 373 375 378 379 380 384 389 390 392 396 400 402 403 408 410 414 418 419 420 423 427 434 435 437 440 441
## 1 1 6 1 1 4 1 6 2 3 3 1 1 1 2 1 1 2 1 1 1 2 29 1 1 1 3 1 1 1 3 1 1 1 1 1 5 1
## 443 449 450 455 459 460 465 466 469 474 480 484 486 490 492 494 499 500 503 509 510 513 514 520 524 530 534 535 542 545 546 553 554 559 568 569 570 585
## 1 1 9 1 2 1 2 1 1 2 2 1 1 1 1 1 1 120 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1
## 600 604 625 636 644 650 656 658 664 669 670 677 687 688 690 694 700 704 705 716 720 724 728 730 750 774 795 800 802 806 810 820 830 840 895 900 950 970
## 12 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 2 1 1 5 1 1 1 1 1 1 1 1 1 1
## 980 1000 1012 1036 1050 1128 1132 1200 1220 1228 1230 1264 1376 1400 1470 1500 1594 1802 1832 1900 2000 2340 3100 3600 5000 <NA>
## 1 19 1 1 2 1 1 1 1 1 1 1 1 1 1 7 1 1 1 1 7 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q36. Q751: In the last 30 days how much did the household spend on Personal items? H
## -998 0 1 7 8 10 12 14 15 16 18 19 20 21
## 3 237 4 1 5 1 4 4 5 4 1 1 15 2
## 22 24 25 27 28 30 31 32 34 35 36 37 38 39
## 2 8 15 3 12 17 1 16 5 10 10 1 1 3
## 40 42 43 44 45 46 47 48 49 50 52 53 54 55
## 24 3 1 3 13 1 3 12 3 58 5 3 1 6
## 56 57 58 59 60 61 62 63 64 65 66 67 68 69
## 13 4 4 2 25 1 2 1 13 8 4 3 3 1
## 70 71 72 73 74 75 76 77 78 79 80 81 82 84
## 22 1 11 1 3 7 3 2 2 4 27 1 1 15
## 85 86 87 88 89 90 91 92 93 94 95 96 98 99
## 2 3 1 3 1 12 2 6 2 4 5 17 3 8
## 100 102 103 104 105 106 107 108 109 110 112 113 114 115
## 142 1 1 3 5 2 1 4 6 6 4 1 3 5
## 116 117 118 119 120 121 122 124 125 126 127 128 129 130
## 1 1 3 8 36 2 1 2 5 3 3 4 1 13
## 131 132 133 135 136 137 138 139 140 141 142 143 144 145
## 2 2 2 6 3 2 2 1 15 3 2 1 3 10
## 147 148 149 150 151 152 154 155 156 158 159 160 163 164
## 2 1 3 86 3 3 4 3 1 1 2 15 1 2
## 165 169 170 171 172 173 174 175 178 179 180 181 182 184
## 3 2 3 1 2 2 3 2 3 5 11 2 3 3
## 186 188 189 190 192 193 194 195 196 197 198 199 200 204
## 1 3 3 6 4 1 1 1 5 4 3 6 167 3
## 206 207 208 209 210 212 213 214 215 216 217 218 219 220
## 1 2 3 3 9 2 2 2 5 2 1 3 3 6
## 222 223 225 227 228 229 230 231 232 233 234 235 236 237
## 1 1 2 3 2 1 8 1 5 1 4 1 3 3
## 238 239 240 241 244 246 248 250 251 252 253 255 256 257
## 4 2 7 1 3 4 1 40 2 1 1 2 3 1
## 259 260 262 263 264 265 267 268 269 270 271 273 275 276
## 4 4 2 1 4 2 2 1 4 4 1 1 1 6
## 277 278 280 282 283 284 285 286 287 288 289 290 292 293
## 1 2 8 1 1 1 1 1 1 2 3 4 3 1
## 294 298 299 300 301 302 305 308 309 310 312 313 314 315
## 1 1 3 148 1 2 1 3 2 2 1 1 2 3
## 316 320 321 322 323 325 326 328 330 331 336 339 340 342
## 1 6 1 2 2 2 1 3 1 2 2 1 3 1
## 345 349 350 354 356 358 359 360 364 367 370 372 373 375
## 1 1 6 1 1 4 1 6 2 3 3 1 1 1
## 378 379 380 384 389 390 392 396 400 402 403 408 410 414
## 2 1 1 2 1 1 1 2 29 1 1 1 3 1
## 418 419 420 423 427 434 435 437 440 441 443 449 450 455
## 1 1 3 1 1 1 1 1 5 1 1 1 9 1
## 459 460 465 466 469 474 480 484 486 490 492 494 499 500
## 2 1 2 1 1 2 2 1 1 1 1 1 1 120
## 503 509 510 513 514 520 524 530 534 535 542 545 546 553
## 1 1 1 1 2 1 1 1 1 1 2 1 1 1
## 554 559 568 569 570 585 600 604 625 636 644 650 656 658
## 1 1 1 1 2 1 12 1 1 1 1 1 1 2
## 664 669 670 677 687 688 690 694 700 704 705 716 720 724
## 1 1 1 1 1 1 1 1 11 1 1 1 1 1
## 728 730 750 774 795 800 802 806 810 820 830 840 895 900
## 1 1 2 1 1 5 1 1 1 1 1 1 1 1
## 950 970 980 1000 1012 1036 1050 1128 1132 1200 1220 1228 1230 1264
## 1 1 1 19 1 1 2 1 1 1 1 1 1 1
## 1376 1400 1470 1500 1594 1802 1832 1870 or more <NA>
## 1 1 1 7 1 1 1 12 1
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q37)[na.exclude(mydata$eh_s10q37)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q37", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q37. Q753: In the last 30 days how much did the household spend on Household items?
## -998 0 1 2 7 10 12 17 18 20 21 24 25 26 30 32 34 35 36 38 40 41 42 43 45 46 48 49 50 52 53 54 55 56 58 60 62 63
## 4 34 4 1 1 1 2 1 1 1 1 3 3 1 2 1 1 3 2 1 2 1 2 2 3 2 2 2 21 3 1 1 1 2 1 9 1 1
## 64 65 67 68 70 72 75 77 78 80 81 82 83 84 85 86 87 88 90 91 92 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
## 2 1 1 2 5 5 1 1 2 6 1 1 2 3 2 4 2 2 4 1 3 2 3 7 2 2 1 114 1 1 1 3 1 4 1 3 2 3
## 111 112 113 114 115 116 118 120 122 123 125 126 127 128 130 132 133 134 135 136 137 138 140 141 143 144 145 146 147 148 150 151 152 154 155 156 158 159
## 2 1 2 1 5 2 2 29 3 1 2 3 2 4 6 2 1 1 2 1 2 3 8 1 1 6 4 2 1 2 79 2 10 4 5 6 2 1
## 160 161 162 163 164 165 166 167 168 169 170 172 174 175 176 177 179 180 181 182 183 184 186 188 189 190 192 193 194 195 196 200 201 202 203 204 205 206
## 21 1 2 1 5 3 1 1 10 3 6 1 4 2 4 1 1 14 2 1 1 3 3 2 1 5 6 1 1 1 1 231 1 1 1 1 1 1
## 207 208 210 211 212 214 215 216 217 218 219 220 221 222 223 224 225 226 228 230 231 232 234 235 236 240 241 242 243 244 245 246 248 249 250 251 252 254
## 1 6 7 2 4 2 2 6 1 1 3 13 2 4 2 3 1 4 1 5 3 5 6 2 2 15 1 2 2 2 3 1 3 1 52 2 2 2
## 255 256 257 258 260 261 262 263 264 267 270 271 272 273 274 275 276 277 278 280 281 284 286 287 288 290 291 292 295 296 298 300 302 303 304 305 306 308
## 1 2 1 3 3 2 1 1 5 1 3 2 3 3 3 3 1 1 1 15 2 2 2 1 2 5 1 4 3 3 2 246 1 2 3 1 2 2
## 309 310 312 313 314 315 316 320 321 322 324 325 328 330 332 334 335 336 337 338 340 342 344 345 346 347 348 350 352 353 355 360 362 363 364 366 368 369
## 1 4 4 2 1 4 2 11 1 2 3 3 5 1 2 2 2 7 1 3 5 1 1 3 2 2 3 14 3 1 1 7 2 1 2 1 3 1
## 370 372 374 375 377 378 379 380 382 384 385 388 389 390 392 394 395 396 398 399 400 402 404 406 408 410 412 415 416 418 420 422 427 430 432 434 435 437
## 2 4 2 1 1 1 1 5 1 3 1 3 1 2 3 3 1 2 1 1 82 1 2 1 1 2 1 1 5 1 5 2 1 2 1 1 1 1
## 438 440 442 448 450 456 457 458 460 461 462 464 465 468 470 472 474 475 476 477 480 482 483 484 485 489 490 495 496 500 506 508 510 512 513 515 520 522
## 1 3 1 2 9 1 1 2 4 1 1 2 1 2 2 2 2 2 1 1 5 1 1 2 1 1 1 1 2 248 1 1 1 6 1 1 3 1
## 524 530 536 540 543 549 550 552 554 558 560 570 572 574 580 584 590 592 600 605 606 608 610 614 616 620 626 632 636 637 640 644 648 651 660 664 669 671
## 1 1 2 1 1 2 3 1 1 1 2 1 1 1 1 2 1 1 36 1 1 2 2 1 2 1 1 1 1 1 2 1 1 1 2 1 1 1
## 672 676 680 684 690 700 706 710 713 724 730 736 745 748 750 751 752 762 764 770 771 772 780 784 794 797 800 808 825 834 840 844 850 880 890 932 940 958
## 1 1 2 1 1 11 1 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 12 1 1 1 2 1 2 1 1 1 1 1
## 960 968 970 974 975 985 1000 1020 1024 1028 1040 1050 1115 1136 1154 1162 1190 1200 1204 1240 1280 1300 1368 1422 1440 1498 1500 1518 1600 1860 2000 2003 2100 2225 2400 2424 2500 2610
## 1 1 1 1 1 1 69 1 1 1 1 1 1 1 1 1 1 7 1 1 1 1 1 1 1 1 20 1 2 1 16 1 1 1 1 1 1 1
## 3000 3250 3500 3540 4800 5000
## 3 1 3 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q37. Q753: In the last 30 days how much did the household spend on Household items?
## -998 0 1 2 7 10 12 17 18 20 21 24 25 26
## 4 34 4 1 1 1 2 1 1 1 1 3 3 1
## 30 32 34 35 36 38 40 41 42 43 45 46 48 49
## 2 1 1 3 2 1 2 1 2 2 3 2 2 2
## 50 52 53 54 55 56 58 60 62 63 64 65 67 68
## 21 3 1 1 1 2 1 9 1 1 2 1 1 2
## 70 72 75 77 78 80 81 82 83 84 85 86 87 88
## 5 5 1 1 2 6 1 1 2 3 2 4 2 2
## 90 91 92 94 95 96 97 98 99 100 101 102 103 104
## 4 1 3 2 3 7 2 2 1 114 1 1 1 3
## 105 106 107 108 109 110 111 112 113 114 115 116 118 120
## 1 4 1 3 2 3 2 1 2 1 5 2 2 29
## 122 123 125 126 127 128 130 132 133 134 135 136 137 138
## 3 1 2 3 2 4 6 2 1 1 2 1 2 3
## 140 141 143 144 145 146 147 148 150 151 152 154 155 156
## 8 1 1 6 4 2 1 2 79 2 10 4 5 6
## 158 159 160 161 162 163 164 165 166 167 168 169 170 172
## 2 1 21 1 2 1 5 3 1 1 10 3 6 1
## 174 175 176 177 179 180 181 182 183 184 186 188 189 190
## 4 2 4 1 1 14 2 1 1 3 3 2 1 5
## 192 193 194 195 196 200 201 202 203 204 205 206 207 208
## 6 1 1 1 1 231 1 1 1 1 1 1 1 6
## 210 211 212 214 215 216 217 218 219 220 221 222 223 224
## 7 2 4 2 2 6 1 1 3 13 2 4 2 3
## 225 226 228 230 231 232 234 235 236 240 241 242 243 244
## 1 4 1 5 3 5 6 2 2 15 1 2 2 2
## 245 246 248 249 250 251 252 254 255 256 257 258 260 261
## 3 1 3 1 52 2 2 2 1 2 1 3 3 2
## 262 263 264 267 270 271 272 273 274 275 276 277 278 280
## 1 1 5 1 3 2 3 3 3 3 1 1 1 15
## 281 284 286 287 288 290 291 292 295 296 298 300 302 303
## 2 2 2 1 2 5 1 4 3 3 2 246 1 2
## 304 305 306 308 309 310 312 313 314 315 316 320 321 322
## 3 1 2 2 1 4 4 2 1 4 2 11 1 2
## 324 325 328 330 332 334 335 336 337 338 340 342 344 345
## 3 3 5 1 2 2 2 7 1 3 5 1 1 3
## 346 347 348 350 352 353 355 360 362 363 364 366 368 369
## 2 2 3 14 3 1 1 7 2 1 2 1 3 1
## 370 372 374 375 377 378 379 380 382 384 385 388 389 390
## 2 4 2 1 1 1 1 5 1 3 1 3 1 2
## 392 394 395 396 398 399 400 402 404 406 408 410 412 415
## 3 3 1 2 1 1 82 1 2 1 1 2 1 1
## 416 418 420 422 427 430 432 434 435 437 438 440 442 448
## 5 1 5 2 1 2 1 1 1 1 1 3 1 2
## 450 456 457 458 460 461 462 464 465 468 470 472 474 475
## 9 1 1 2 4 1 1 2 1 2 2 2 2 2
## 476 477 480 482 483 484 485 489 490 495 496 500 506 508
## 1 1 5 1 1 2 1 1 1 1 2 248 1 1
## 510 512 513 515 520 522 524 530 536 540 543 549 550 552
## 1 6 1 1 3 1 1 1 2 1 1 2 3 1
## 554 558 560 570 572 574 580 584 590 592 600 605 606 608
## 1 1 2 1 1 1 1 2 1 1 36 1 1 2
## 610 614 616 620 626 632 636 637 640 644 648 651 660 664
## 2 1 2 1 1 1 1 1 2 1 1 1 2 1
## 669 671 672 676 680 684 690 700 706 710 713 724 730 736
## 1 1 1 1 2 1 1 11 1 1 1 2 2 1
## 745 748 750 751 752 762 764 770 771 772 780 784 794 797
## 1 1 2 1 1 1 1 1 1 1 1 1 1 1
## 800 808 825 834 840 844 850 880 890 932 940 958 960 968
## 12 1 1 1 2 1 2 1 1 1 1 1 1 1
## 970 974 975 985 1000 1020 1024 1028 1040 1050 1115 1136 1154 1162
## 1 1 1 1 69 1 1 1 1 1 1 1 1 1
## 1190 1200 1204 1240 1280 1300 1368 1422 1440 1498 1500 1518 1600 1860
## 1 7 1 1 1 1 1 1 1 1 20 1 2 1
## 2000 2003 2100 2225 2400 2424 2466 or more
## 16 1 1 1 1 1 12
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q38)[na.exclude(mydata$eh_s10q38)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q38", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q38. Q755: In the last 30 days how much did the household spend on Firewood, kerosene
## -999 0 5 10 15 20 21 24 25 28 30 35 36 40 43 50 56 60 64 65 70 72 75 80 84 92 100 110 120 125 130 135 140 144 150 160 167 168
## 2 1151 1 2 2 11 1 1 4 3 17 2 1 8 1 14 1 5 1 1 2 1 1 9 1 1 29 2 5 1 2 1 10 2 29 8 3 2
## 170 172 180 185 190 196 200 210 217 220 225 228 229 230 240 250 252 255 260 270 278 280 281 285 288 290 298 300 305 310 313 320 325 330 335 340 350 355
## 3 1 9 1 2 1 57 2 2 9 2 1 1 3 10 27 1 1 3 2 1 8 1 1 1 1 1 50 1 2 1 9 4 8 1 3 20 2
## 360 375 380 400 405 418 420 440 444 450 460 470 475 480 490 500 510 515 520 530 540 545 548 550 555 560 566 570 575 580 585 590 600 610 614 615 617 620
## 9 3 1 36 1 1 3 6 1 13 1 1 1 3 4 65 5 1 16 18 25 2 1 26 1 22 1 10 2 10 1 3 89 4 2 2 1 14
## 625 630 631 635 637 638 640 645 650 655 657 660 665 670 672 673 675 677 680 685 690 700 705 710 720 721 730 740 750 751 755 760 765 770 775 780 782 790
## 1 13 1 1 1 1 7 2 34 1 1 3 1 8 1 1 2 1 21 1 5 39 1 2 6 1 1 1 12 1 1 4 1 2 1 5 1 1
## 800 840 850 855 870 900 916 930 1000 1020 1035 1050 1080 1100 1110 1120 1160 1180 1200 1232 1300 1350 1400 1440 1500 1575 1800 2000 2100 2200 2600 2800 3200 3900 6660 <NA>
## 11 4 3 1 1 23 1 2 11 2 1 2 2 1 1 2 1 1 10 1 1 3 1 2 5 1 1 1 2 1 1 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q38. Q755: In the last 30 days how much did the household spend on Firewood, kerosene
## -999 0 5 10 15 20 21 24 25 28 30 35 36 40
## 2 1151 1 2 2 11 1 1 4 3 17 2 1 8
## 43 50 56 60 64 65 70 72 75 80 84 92 100 110
## 1 14 1 5 1 1 2 1 1 9 1 1 29 2
## 120 125 130 135 140 144 150 160 167 168 170 172 180 185
## 5 1 2 1 10 2 29 8 3 2 3 1 9 1
## 190 196 200 210 217 220 225 228 229 230 240 250 252 255
## 2 1 57 2 2 9 2 1 1 3 10 27 1 1
## 260 270 278 280 281 285 288 290 298 300 305 310 313 320
## 3 2 1 8 1 1 1 1 1 50 1 2 1 9
## 325 330 335 340 350 355 360 375 380 400 405 418 420 440
## 4 8 1 3 20 2 9 3 1 36 1 1 3 6
## 444 450 460 470 475 480 490 500 510 515 520 530 540 545
## 1 13 1 1 1 3 4 65 5 1 16 18 25 2
## 548 550 555 560 566 570 575 580 585 590 600 610 614 615
## 1 26 1 22 1 10 2 10 1 3 89 4 2 2
## 617 620 625 630 631 635 637 638 640 645 650 655 657 660
## 1 14 1 13 1 1 1 1 7 2 34 1 1 3
## 665 670 672 673 675 677 680 685 690 700 705 710 720 721
## 1 8 1 1 2 1 21 1 5 39 1 2 6 1
## 730 740 750 751 755 760 765 770 775 780 782 790 800 840
## 1 1 12 1 1 4 1 2 1 5 1 1 11 4
## 850 855 870 900 916 930 1000 1020 1035 1050 1080 1100 1110 1120
## 3 1 1 23 1 2 11 2 1 2 2 1 1 2
## 1160 1180 1200 1232 1300 1350 1400 1440 1500 or more <NA>
## 1 1 10 1 1 3 1 2 16 1
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q39)[na.exclude(mydata$eh_s10q39)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q39", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q39. Q757: In the last 30 days how much did the household spend on Electricity ? Sa
## -998 0 10 11 14 18 20 21 22 27 28 30 35 39 40 41 44 45 47 48 49 50 51 53 54 55 57 58 59 60 62 63 64 65 66 67 68 69
## 4 264 1 1 1 1 3 1 2 1 3 4 5 2 3 1 1 3 2 1 1 19 1 1 1 2 2 1 1 10 1 4 2 5 2 1 7 2
## 70 72 73 74 75 76 78 80 81 82 84 85 86 88 89 90 91 92 93 94 95 96 98 99 100 104 107 110 112 115 117 118 119 120 121 126 129 130
## 8 2 1 2 5 3 3 9 5 4 1 6 2 1 1 4 3 2 2 1 1 3 1 1 58 1 3 11 3 2 1 2 2 12 1 1 1 11
## 132 133 135 136 140 142 146 147 150 151 152 154 155 156 160 163 165 167 168 169 170 172 174 175 176 177 178 180 185 187 188 189 190 195 197 200 201 204
## 1 1 2 1 3 1 2 1 43 1 1 1 1 2 7 1 1 1 3 1 8 1 2 1 2 1 1 10 3 2 1 3 1 3 3 92 1 1
## 205 206 207 208 209 210 214 216 217 218 220 221 224 225 226 227 228 229 230 231 232 234 235 236 237 238 239 240 242 245 248 250 251 253 254 256 257 258
## 1 1 2 1 1 4 1 2 2 1 7 2 2 8 5 1 1 1 11 2 1 1 4 1 1 2 1 7 1 1 1 55 1 1 2 3 2 1
## 259 260 262 263 264 265 267 269 270 272 275 276 279 280 281 285 286 289 290 291 292 294 295 300 303 304 305 306 307 308 310 312 313 315 316 317 320 324
## 1 11 1 1 1 3 1 2 3 2 4 1 1 7 1 4 1 1 6 1 1 1 1 132 2 1 1 2 1 2 3 4 1 2 1 4 8 1
## 328 329 330 332 334 336 337 338 340 343 345 346 347 349 350 352 355 356 357 358 359 360 361 362 363 365 369 370 373 374 375 376 378 379 380 381 382 383
## 1 2 1 1 1 2 1 1 6 1 1 2 2 1 34 1 3 3 4 1 1 9 1 1 2 6 1 5 2 1 4 2 2 1 13 1 2 1
## 385 387 389 390 395 398 399 400 405 406 408 410 414 420 422 423 425 426 427 430 432 434 435 436 438 440 442 444 445 446 447 448 450 456 458 460 464 468
## 2 1 1 2 2 4 2 88 2 2 1 1 1 3 1 1 1 2 1 3 1 2 1 1 1 3 1 2 1 1 1 1 24 1 2 3 2 1
## 469 470 474 475 476 479 480 481 483 484 486 487 490 491 492 495 496 499 500 501 507 509 510 515 516 518 520 521 523 526 528 529 530 532 535 537 538 539
## 1 5 1 3 1 1 10 2 1 1 1 1 1 1 2 1 1 1 150 1 1 1 3 1 1 1 6 1 1 1 1 1 5 1 1 1 1 1
## 540 541 544 545 550 553 554 555 556 560 563 564 565 566 568 570 575 576 580 582 585 586 587 592 597 600 610 611 612 613 621 630 633 634 639 640 643 645
## 5 1 1 1 8 2 1 2 3 4 1 1 2 1 2 5 3 1 5 1 1 1 1 1 2 69 1 3 1 2 1 3 1 3 1 3 1 1
## 647 648 650 653 655 665 667 670 673 679 680 686 700 710 720 725 728 730 731 732 735 745 748 750 756 759 760 765 766 767 770 780 781 784 789 792 798 799
## 1 1 14 1 1 1 1 3 1 1 2 1 78 2 5 1 1 1 1 1 1 1 1 12 1 1 3 1 1 1 3 3 1 1 1 1 1 1
## 800 802 815 817 820 824 828 830 834 848 850 853 860 870 882 883 886 889 900 920 924 925 940 943 950 970 971 979 980 999 1000 1003 1024 1033 1049 1060 1064 1085
## 53 1 1 1 1 1 1 1 1 1 2 1 1 3 1 1 1 1 16 1 2 1 1 1 2 1 1 1 2 1 48 1 1 1 1 1 1 1
## 1100 1108 1123 1125 1141 1150 1165 1180 1200 1222 1226 1260 1268 1270 1275 1280 1300 1307 1400 1450 1469 1470 1500 1600 1630 1650 1651 1700 1800 1850 1858 1870 1900 1966 2000 2100 2200 2500
## 20 1 1 1 1 2 1 1 34 1 1 1 1 1 1 1 21 1 15 1 1 1 27 7 1 1 1 5 12 1 1 1 2 1 8 4 4 2
## 2600 2776 2800 3000 3200 3500 4000 4050 4300 5000 6000
## 1 1 2 3 1 1 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q39. Q757: In the last 30 days how much did the household spend on Electricity ? Sa
## -998 0 10 11 14 18 20 21 22 27 28 30 35 39
## 4 264 1 1 1 1 3 1 2 1 3 4 5 2
## 40 41 44 45 47 48 49 50 51 53 54 55 57 58
## 3 1 1 3 2 1 1 19 1 1 1 2 2 1
## 59 60 62 63 64 65 66 67 68 69 70 72 73 74
## 1 10 1 4 2 5 2 1 7 2 8 2 1 2
## 75 76 78 80 81 82 84 85 86 88 89 90 91 92
## 5 3 3 9 5 4 1 6 2 1 1 4 3 2
## 93 94 95 96 98 99 100 104 107 110 112 115 117 118
## 2 1 1 3 1 1 58 1 3 11 3 2 1 2
## 119 120 121 126 129 130 132 133 135 136 140 142 146 147
## 2 12 1 1 1 11 1 1 2 1 3 1 2 1
## 150 151 152 154 155 156 160 163 165 167 168 169 170 172
## 43 1 1 1 1 2 7 1 1 1 3 1 8 1
## 174 175 176 177 178 180 185 187 188 189 190 195 197 200
## 2 1 2 1 1 10 3 2 1 3 1 3 3 92
## 201 204 205 206 207 208 209 210 214 216 217 218 220 221
## 1 1 1 1 2 1 1 4 1 2 2 1 7 2
## 224 225 226 227 228 229 230 231 232 234 235 236 237 238
## 2 8 5 1 1 1 11 2 1 1 4 1 1 2
## 239 240 242 245 248 250 251 253 254 256 257 258 259 260
## 1 7 1 1 1 55 1 1 2 3 2 1 1 11
## 262 263 264 265 267 269 270 272 275 276 279 280 281 285
## 1 1 1 3 1 2 3 2 4 1 1 7 1 4
## 286 289 290 291 292 294 295 300 303 304 305 306 307 308
## 1 1 6 1 1 1 1 132 2 1 1 2 1 2
## 310 312 313 315 316 317 320 324 328 329 330 332 334 336
## 3 4 1 2 1 4 8 1 1 2 1 1 1 2
## 337 338 340 343 345 346 347 349 350 352 355 356 357 358
## 1 1 6 1 1 2 2 1 34 1 3 3 4 1
## 359 360 361 362 363 365 369 370 373 374 375 376 378 379
## 1 9 1 1 2 6 1 5 2 1 4 2 2 1
## 380 381 382 383 385 387 389 390 395 398 399 400 405 406
## 13 1 2 1 2 1 1 2 2 4 2 88 2 2
## 408 410 414 420 422 423 425 426 427 430 432 434 435 436
## 1 1 1 3 1 1 1 2 1 3 1 2 1 1
## 438 440 442 444 445 446 447 448 450 456 458 460 464 468
## 1 3 1 2 1 1 1 1 24 1 2 3 2 1
## 469 470 474 475 476 479 480 481 483 484 486 487 490 491
## 1 5 1 3 1 1 10 2 1 1 1 1 1 1
## 492 495 496 499 500 501 507 509 510 515 516 518 520 521
## 2 1 1 1 150 1 1 1 3 1 1 1 6 1
## 523 526 528 529 530 532 535 537 538 539 540 541 544 545
## 1 1 1 1 5 1 1 1 1 1 5 1 1 1
## 550 553 554 555 556 560 563 564 565 566 568 570 575 576
## 8 2 1 2 3 4 1 1 2 1 2 5 3 1
## 580 582 585 586 587 592 597 600 610 611 612 613 621 630
## 5 1 1 1 1 1 2 69 1 3 1 2 1 3
## 633 634 639 640 643 645 647 648 650 653 655 665 667 670
## 1 3 1 3 1 1 1 1 14 1 1 1 1 3
## 673 679 680 686 700 710 720 725 728 730 731 732 735 745
## 1 1 2 1 78 2 5 1 1 1 1 1 1 1
## 748 750 756 759 760 765 766 767 770 780 781 784 789 792
## 1 12 1 1 3 1 1 1 3 3 1 1 1 1
## 798 799 800 802 815 817 820 824 828 830 834 848 850 853
## 1 1 53 1 1 1 1 1 1 1 1 1 2 1
## 860 870 882 883 886 889 900 920 924 925 940 943 950 970
## 1 3 1 1 1 1 16 1 2 1 1 1 2 1
## 971 979 980 999 1000 1003 1024 1033 1049 1060 1064 1085 1100 1108
## 1 1 2 1 48 1 1 1 1 1 1 1 20 1
## 1123 1125 1141 1150 1165 1180 1200 1222 1226 1260 1268 1270 1275 1280
## 1 1 1 2 1 1 34 1 1 1 1 1 1 1
## 1300 1307 1400 1450 1469 1470 1500 1600 1630 1650 1651 1700 1800 1850
## 21 1 15 1 1 1 27 7 1 1 1 5 12 1
## 1858 1870 1900 1966 2000 2100 2200 2500 2600 2776 2789 or more
## 1 1 2 1 8 4 4 2 1 1 12
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q40)[na.exclude(mydata$eh_s10q40)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q40", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q40. Q759: In the last 30 days how much did the household spend on Water ? Sa nakali
## -999 -998 0 1 4 6 10 12 15 16 20 21 23 24 25 26 30 31 32 35 40 42 45 48 50 55 56 60 64 69 70 72 75 80 84 90 95 100
## 1 3 1328 1 1 1 14 2 2 1 47 1 1 3 2 2 23 1 2 9 11 1 3 1 31 1 1 17 2 1 3 3 6 14 1 10 1 58
## 105 107 110 112 116 117 120 125 126 127 130 132 133 137 139 140 144 150 151 152 156 160 163 164 167 168 169 170 171 173 175 180 181 190 196 200 203 204
## 1 1 3 2 1 1 17 8 1 1 9 1 1 2 1 14 1 50 1 1 1 8 8 2 1 1 1 6 1 1 2 12 1 4 1 65 1 5
## 210 216 219 220 224 225 226 230 232 234 235 236 238 240 245 246 247 248 249 250 253 259 260 263 268 273 274 275 280 286 288 291 295 300 304 308 315 316
## 4 1 1 4 2 2 4 1 2 1 2 4 1 11 1 1 1 1 1 42 1 1 5 1 1 1 1 2 7 1 1 5 1 57 1 2 1 1
## 318 320 324 327 330 331 338 340 350 352 355 360 370 375 379 389 400 410 413 420 430 435 449 450 452 455 467 480 494 500 510 520 529 533 535 540 550 560
## 1 4 1 1 3 1 1 1 19 1 1 8 2 4 1 1 40 2 1 3 2 1 1 2 1 1 1 4 1 32 1 1 1 1 2 2 3 5
## 568 575 600 630 640 643 644 650 664 675 700 720 750 752 760 780 800 840 847 879 900 960 1000 1001 1050 1060 1065 1200 1300 1440 1700 1920 2000 2100 2160 2700 2800 3920
## 1 1 25 1 1 1 1 3 1 1 9 2 5 1 1 1 7 1 1 1 2 3 6 1 2 1 1 10 3 1 1 1 1 1 1 1 1 1
## <NA>
## 2
## [1] "Frequency table after encoding"
## eh_s10q40. Q759: In the last 30 days how much did the household spend on Water ? Sa nakali
## -999 -998 0 1 4 6 10 12 15 16 20 21 23 24
## 1 3 1328 1 1 1 14 2 2 1 47 1 1 3
## 25 26 30 31 32 35 40 42 45 48 50 55 56 60
## 2 2 23 1 2 9 11 1 3 1 31 1 1 17
## 64 69 70 72 75 80 84 90 95 100 105 107 110 112
## 2 1 3 3 6 14 1 10 1 58 1 1 3 2
## 116 117 120 125 126 127 130 132 133 137 139 140 144 150
## 1 1 17 8 1 1 9 1 1 2 1 14 1 50
## 151 152 156 160 163 164 167 168 169 170 171 173 175 180
## 1 1 1 8 8 2 1 1 1 6 1 1 2 12
## 181 190 196 200 203 204 210 216 219 220 224 225 226 230
## 1 4 1 65 1 5 4 1 1 4 2 2 4 1
## 232 234 235 236 238 240 245 246 247 248 249 250 253 259
## 2 1 2 4 1 11 1 1 1 1 1 42 1 1
## 260 263 268 273 274 275 280 286 288 291 295 300 304 308
## 5 1 1 1 1 2 7 1 1 5 1 57 1 2
## 315 316 318 320 324 327 330 331 338 340 350 352 355 360
## 1 1 1 4 1 1 3 1 1 1 19 1 1 8
## 370 375 379 389 400 410 413 420 430 435 449 450 452 455
## 2 4 1 1 40 2 1 3 2 1 1 2 1 1
## 467 480 494 500 510 520 529 533 535 540 550 560 568 575
## 1 4 1 32 1 1 1 1 2 2 3 5 1 1
## 600 630 640 643 644 650 664 675 700 720 750 752 760 780
## 25 1 1 1 1 3 1 1 9 2 5 1 1 1
## 800 840 847 879 900 960 1000 1001 1050 1060 1065 1200 1257 or more <NA>
## 7 1 1 1 2 3 6 1 2 1 1 10 12 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q41)[na.exclude(mydata$eh_s10q41)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q41", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q41. Q761: In the last 30 days how much did the household spend on House rent/mortgag
## -999 0 20 50 100 150 171 200 280 300 317 360 400 450 500 600 700 713 750 800 850 900 1000 1200 1500 1600 1700 2000 2500 3000 3500 3800
## 2 2188 1 3 3 1 1 3 1 3 1 1 1 1 12 5 1 1 1 2 1 1 9 4 7 2 3 10 5 2 3 1
## 5000 6750 10000 13000 <NA>
## 2 1 2 1 2
## [1] "Frequency table after encoding"
## eh_s10q41. Q761: In the last 30 days how much did the household spend on House rent/mortgag
## -999 0 20 50 100 150 171 200 280 300 317 360 400 450
## 2 2188 1 3 3 1 1 3 1 3 1 1 1 1
## 500 600 700 713 750 800 850 900 1000 1200 1500 1600 1700 2000
## 12 5 1 1 1 2 1 1 9 4 7 2 3 10
## 2500 2787 or more <NA>
## 5 12 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q42)[na.exclude(mydata$eh_s10q42)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q42", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q42. Q763: In the last 30 days how much did the household spend on Fixing home damage
## -999 -998 0 40 95 100 110 140 175 180 200 260 280 300 350 400 440 450 500 510 650 700 730 750 800 900 1000
## 3 2 2058 1 1 2 1 1 1 1 4 1 1 4 1 2 1 2 6 1 1 4 1 1 1 1 9
## 1010 1200 1300 1500 1530 1600 1800 1875 1920 2000 2185 2200 2214 2300 2400 2500 2800 3000 3200 3300 3500 3800 4000 4500 4518 5000 5800
## 1 1 1 8 1 2 1 1 1 16 1 2 1 1 3 6 1 8 1 1 3 1 5 1 1 17 1
## 6000 7000 7700 8000 9000 10000 11000 12000 13000 14000 15000 15250 16000 17000 20000 22000 25000 27000 28000 30000 33000 35000 40000 50000 55000 80000 90000
## 7 8 1 4 1 9 1 1 3 2 6 1 1 1 11 1 3 1 1 7 2 2 2 5 1 2 1
## 1e+05 150000 <NA>
## 1 1 2
## [1] "Frequency table after encoding"
## eh_s10q42. Q763: In the last 30 days how much did the household spend on Fixing home damage
## -999 -998 0 40 95 100 110 140 175 180 200 260 280
## 3 2 2058 1 1 2 1 1 1 1 4 1 1
## 300 350 400 440 450 500 510 650 700 730 750 800 900
## 4 1 2 1 2 6 1 1 4 1 1 1 1
## 1000 1010 1200 1300 1500 1530 1600 1800 1875 1920 2000 2185 2200
## 9 1 1 1 8 1 2 1 1 1 16 1 2
## 2214 2300 2400 2500 2800 3000 3200 3300 3500 3800 4000 4500 4518
## 1 1 3 6 1 8 1 1 3 1 5 1 1
## 5000 5800 6000 7000 7700 8000 9000 10000 11000 12000 13000 14000 15000
## 17 1 7 8 1 4 1 9 1 1 3 2 6
## 15250 16000 17000 20000 22000 25000 27000 28000 30000 33000 35000 40000 or more <NA>
## 1 1 1 11 1 3 1 1 7 2 2 13 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q43)[na.exclude(mydata$eh_s10q43)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q43", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q43. Q765: In the last 30 days how much did the household spend on Religious expenses
## -999 0 2 4 5 7 10 12 15 20 25 28 30 35 36 40 50 52 60 64 65 70 75 80 92 100 120 132 150 160 200 240
## 2 1593 1 1 11 1 23 2 2 138 2 1 19 1 1 57 59 1 10 1 1 5 2 69 1 97 10 1 11 2 55 5
## 250 280 300 310 320 350 400 480 500 550 600 630 640 700 750 800 880 900 1000 1100 1200 1500 1700 1920 2000 2500 2600 3000 3500 4000 5000 8000
## 2 1 12 1 2 1 14 1 19 1 4 1 1 1 1 5 1 1 8 1 1 4 2 1 2 1 1 2 1 1 2 1
## 10000 20000 30000 35000 <NA>
## 2 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q43. Q765: In the last 30 days how much did the household spend on Religious expenses
## -999 0 2 4 5 7 10 12 15 20 25 28 30 35
## 2 1593 1 1 11 1 23 2 2 138 2 1 19 1
## 36 40 50 52 60 64 65 70 75 80 92 100 120 132
## 1 57 59 1 10 1 1 5 2 69 1 97 10 1
## 150 160 200 240 250 280 300 310 320 350 400 480 500 550
## 11 2 55 5 2 1 12 1 2 1 14 1 19 1
## 600 630 640 700 750 800 880 900 1000 1100 1200 1500 1700 1920
## 4 1 1 1 1 5 1 1 8 1 1 4 2 1
## 2000 2500 2600 2828 or more <NA>
## 2 1 1 12 1
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q44)[na.exclude(mydata$eh_s10q44)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q44", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q44. Q767: In the last 30 days how much did the household spend on Charitable donatio
## -999 -998 0 5 8 10 15 18 20 25 30 35 40 50 55 60 70 80 92 100 105 110 120 130 150 160 180 200 250 300 350 400
## 1 1 1702 9 2 26 5 1 126 3 12 2 27 74 1 11 5 32 1 104 1 1 7 1 15 2 1 48 2 18 1 10
## 480 500 600 700 1000 1200 1500 2000 2400 5600 6000 35000 <NA>
## 1 15 2 1 8 1 2 1 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q44. Q767: In the last 30 days how much did the household spend on Charitable donatio
## -999 -998 0 5 8 10 15 18 20 25 30 35 40 50
## 1 1 1702 9 2 26 5 1 126 3 12 2 27 74
## 55 60 70 80 92 100 105 110 120 130 150 160 180 200
## 1 11 5 32 1 104 1 1 7 1 15 2 1 48
## 250 300 350 400 480 500 600 700 1000 or more <NA>
## 2 18 1 10 1 15 2 1 16 1
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q45)[na.exclude(mydata$eh_s10q45)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q45", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q45. Q769: In the last 30 days how much did the household spend on Weddings ? Sa nak
## -999 -998 0 8 20 30 50 100 120 150 180 200 250 280 300 350 360 400 450 500 550 600 650 700 750 980 1000 1100 1500 2000 2500 3000
## 2 1 2093 1 4 1 5 24 2 10 2 36 2 1 11 1 1 8 1 30 1 1 3 6 1 1 10 1 4 4 2 4
## 3500 4000 4200 6000 6676 12000 20000 30000 50000 1e+05 <NA>
## 1 1 1 2 1 1 2 1 1 1 2
## [1] "Frequency table after encoding"
## eh_s10q45. Q769: In the last 30 days how much did the household spend on Weddings ? Sa nak
## -999 -998 0 8 20 30 50 100 120 150 180 200 250 280
## 2 1 2093 1 4 1 5 24 2 10 2 36 2 1
## 300 350 360 400 450 500 550 600 650 700 750 980 1000 1100
## 11 1 1 8 1 30 1 1 3 6 1 1 10 1
## 1500 2000 2500 3000 3287 or more <NA>
## 4 4 2 4 12 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q46)[na.exclude(mydata$eh_s10q46)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q46", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q46. Q771: In the last 30 days how much did the household spend on Funerals (includin
## -999 0 10 15 20 25 30 40 50 54 60 65 70 80 100 120 129 140 150 160 200 210 300 301 400 420 450 500 600 975 1000 1500
## 1 1996 14 1 42 2 3 10 33 1 4 1 3 2 69 1 1 1 2 1 28 1 13 1 4 1 1 14 1 1 14 1
## 1650 2000 3000 4700 5000 9000 10000 12000 13000 20000 20700 30000 44000 58000 <NA>
## 1 2 1 1 4 1 1 1 1 1 1 1 1 1 2
## [1] "Frequency table after encoding"
## eh_s10q46. Q771: In the last 30 days how much did the household spend on Funerals (includin
## -999 0 10 15 20 25 30 40 50 54 60 65 70 80
## 1 1996 14 1 42 2 3 10 33 1 4 1 3 2
## 100 120 129 140 150 160 200 210 300 301 400 420 450 500
## 69 1 1 1 2 1 28 1 13 1 4 1 1 14
## 600 975 1000 1500 1650 2000 3000 4700 5000 or more <NA>
## 1 1 14 1 1 2 1 1 13 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q47)[na.exclude(mydata$eh_s10q47)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q47", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q47. Q773: In the last 30 days how much did the household spend on School/college fee
## -998 0 7 8 10 15 20 25 28 30 36 40 42 50 52 56 60 65 67 70 80 90 92 100 105 110 120 125 130 135 136 140
## 3 1127 1 1 3 1 6 1 1 7 2 1 1 26 1 1 5 1 1 2 3 2 1 47 1 1 10 1 1 3 1 2
## 150 155 160 165 170 175 180 187 190 200 220 225 227 230 232 240 250 260 265 270 275 280 283 285 290 295 300 305 310 320 324 325
## 37 1 8 1 1 1 7 1 2 63 3 1 1 3 1 2 16 7 1 1 1 2 1 1 2 2 45 1 4 3 1 1
## 329 340 350 360 370 375 380 385 390 395 399 400 410 420 425 440 445 450 455 457 460 466 475 480 485 486 490 494 495 500 505 510
## 1 3 26 8 1 4 5 1 2 1 1 45 3 2 1 1 1 20 2 1 3 1 1 3 1 1 2 1 1 73 1 2
## 515 520 525 535 550 560 565 568 570 575 580 600 610 615 626 650 655 658 660 667 670 675 678 680 685 690 700 710 725 737 740 750
## 1 2 1 2 6 4 1 1 2 4 4 28 2 1 1 6 1 1 1 1 1 1 1 1 2 1 25 1 2 2 1 9
## 780 781 800 815 820 840 849 850 860 865 870 880 886 900 910 940 948 950 960 970 980 1000 1009 1020 1021 1030 1040 1045 1050 1068 1100 1110
## 3 1 23 1 1 1 1 8 2 1 1 2 1 9 1 1 1 3 1 1 2 57 1 2 1 1 1 1 1 1 5 1
## 1125 1170 1180 1200 1240 1250 1280 1290 1300 1325 1375 1380 1383 1400 1430 1450 1455 1480 1490 1500 1530 1595 1600 1655 1660 1697 1700 1750 1800 1825 1840 1880
## 1 1 2 24 1 3 1 1 10 1 1 2 1 6 1 2 1 1 1 44 1 1 4 1 1 1 4 1 8 1 1 1
## 1890 2000 2045 2060 2100 2156 2200 2250 2300 2360 2400 2420 2450 2500 2584 2590 2600 2660 2700 2750 2760 2800 2850 2910 2960 3000 3150 3260 3297 3300 3350 3400
## 1 37 1 1 2 1 3 1 3 1 1 1 2 8 1 1 1 1 2 1 1 2 1 1 1 36 1 1 1 2 1 1
## 3465 3500 3600 3730 4000 4100 4200 4250 4320 4380 4385 4500 4900 4965 5000 5200 5300 5400 5500 5551 5765 5800 6000 6450 6500 6600 6825 7700 8010 8700 9000 9200
## 1 4 3 1 10 1 1 1 1 1 1 1 1 1 8 1 1 1 2 1 1 2 2 1 1 2 1 1 1 1 2 1
## 9500 10000 10230 10440 11800 11895 12000 12750 13000 13150 14400 14500 17600 21000 27000 <NA>
## 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## [1] "Frequency table after encoding"
## eh_s10q47. Q773: In the last 30 days how much did the household spend on School/college fee
## -998 0 7 8 10 15 20 25 28 30 36 40 42
## 3 1127 1 1 3 1 6 1 1 7 2 1 1
## 50 52 56 60 65 67 70 80 90 92 100 105 110
## 26 1 1 5 1 1 2 3 2 1 47 1 1
## 120 125 130 135 136 140 150 155 160 165 170 175 180
## 10 1 1 3 1 2 37 1 8 1 1 1 7
## 187 190 200 220 225 227 230 232 240 250 260 265 270
## 1 2 63 3 1 1 3 1 2 16 7 1 1
## 275 280 283 285 290 295 300 305 310 320 324 325 329
## 1 2 1 1 2 2 45 1 4 3 1 1 1
## 340 350 360 370 375 380 385 390 395 399 400 410 420
## 3 26 8 1 4 5 1 2 1 1 45 3 2
## 425 440 445 450 455 457 460 466 475 480 485 486 490
## 1 1 1 20 2 1 3 1 1 3 1 1 2
## 494 495 500 505 510 515 520 525 535 550 560 565 568
## 1 1 73 1 2 1 2 1 2 6 4 1 1
## 570 575 580 600 610 615 626 650 655 658 660 667 670
## 2 4 4 28 2 1 1 6 1 1 1 1 1
## 675 678 680 685 690 700 710 725 737 740 750 780 781
## 1 1 1 2 1 25 1 2 2 1 9 3 1
## 800 815 820 840 849 850 860 865 870 880 886 900 910
## 23 1 1 1 1 8 2 1 1 2 1 9 1
## 940 948 950 960 970 980 1000 1009 1020 1021 1030 1040 1045
## 1 1 3 1 1 2 57 1 2 1 1 1 1
## 1050 1068 1100 1110 1125 1170 1180 1200 1240 1250 1280 1290 1300
## 1 1 5 1 1 1 2 24 1 3 1 1 10
## 1325 1375 1380 1383 1400 1430 1450 1455 1480 1490 1500 1530 1595
## 1 1 2 1 6 1 2 1 1 1 44 1 1
## 1600 1655 1660 1697 1700 1750 1800 1825 1840 1880 1890 2000 2045
## 4 1 1 1 4 1 8 1 1 1 1 37 1
## 2060 2100 2156 2200 2250 2300 2360 2400 2420 2450 2500 2584 2590
## 1 2 1 3 1 3 1 1 1 2 8 1 1
## 2600 2660 2700 2750 2760 2800 2850 2910 2960 3000 3150 3260 3297
## 1 1 2 1 1 2 1 1 1 36 1 1 1
## 3300 3350 3400 3465 3500 3600 3730 4000 4100 4200 4250 4320 4380
## 2 1 1 1 4 3 1 10 1 1 1 1 1
## 4385 4500 4900 4965 5000 5200 5300 5400 5500 5551 5765 5800 6000
## 1 1 1 1 8 1 1 1 2 1 1 2 2
## 6450 6500 6600 6825 7700 8010 8700 9000 9200 9500 10000 10230 10350 or more
## 1 1 2 1 1 1 1 2 1 1 5 1 12
## <NA>
## 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q48)[na.exclude(mydata$eh_s10q48)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q48", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q48. Q775: In the last 30 days how much did the household spend on Medical expenses,
## -999 -998 0 5 6 7 10 12 14 15 18 20 24 26 30 32 35 40 45 46 48 50 52 55 60 64 65 66 67 68 70 72
## 1 2 1438 2 2 1 6 1 1 1 1 17 1 1 5 2 6 11 2 2 1 25 1 2 7 1 2 1 1 1 4 1
## 73 75 76 78 80 82 83 88 90 93 100 102 105 106 108 110 111 115 119 120 126 129 130 135 136 140 142 150 154 156 160 162
## 1 2 1 1 3 1 1 1 5 1 26 1 1 2 1 3 1 2 1 6 1 2 5 1 1 2 1 13 1 1 3 1
## 163 170 176 180 185 190 194 195 196 200 210 215 220 224 231 235 236 240 242 250 251 260 266 270 275 280 289 290 299 300 307 310
## 1 1 1 5 1 1 1 1 1 30 2 1 3 1 1 1 1 4 1 8 1 2 1 1 1 1 1 1 1 19 1 1
## 320 340 350 354 365 367 368 378 390 400 415 420 449 450 456 460 470 474 480 500 506 510 512 516 520 540 550 560 562 575 580 600
## 5 1 9 1 1 2 1 1 2 17 1 2 1 6 1 1 2 1 1 34 1 2 1 1 1 1 5 2 1 1 1 6
## 608 630 635 640 650 660 666 676 677 683 686 690 696 698 700 710 711 744 750 755 760 762 767 790 792 800 830 840 850 862 870 878
## 1 1 1 1 4 1 1 1 1 1 1 1 1 1 12 1 1 1 4 1 1 1 1 1 1 12 2 1 2 1 2 1
## 880 896 900 920 930 950 960 970 980 1000 1035 1050 1064 1100 1150 1200 1240 1250 1252 1260 1273 1300 1310 1320 1329 1342 1350 1365 1368 1400 1430 1495
## 1 1 5 2 1 4 1 1 1 40 1 2 1 2 2 16 1 2 1 1 1 5 1 1 1 1 3 1 1 5 1 1
## 1500 1560 1566 1575 1600 1620 1633 1640 1650 1660 1700 1725 1740 1750 1800 1820 1895 1900 1917 1950 2000 2016 2050 2064 2070 2100 2120 2200 2240 2250 2270 2300
## 26 1 1 1 2 1 1 1 1 1 2 1 1 1 8 1 1 1 1 2 21 1 1 1 1 2 1 2 1 1 1 1
## 2342 2350 2400 2430 2500 2532 2648 2650 2678 2700 2726 2750 2787 2800 2805 2880 2900 3000 3050 3060 3100 3165 3180 3200 3292 3392 3400 3450 3500 3519 3578 3600
## 1 1 3 1 12 2 1 1 1 1 1 1 1 2 1 1 2 15 1 1 1 1 1 3 1 1 1 1 2 1 1 2
## 3645 3780 3900 4000 4100 4150 4200 4310 4396 4500 4600 4680 4750 5000 5140 5430 5500 5520 5800 6000 6300 6313 6500 6620 6680 7000 7300 7330 7406 7500 7900 7973
## 1 1 2 10 2 1 1 1 1 6 2 1 1 13 1 1 2 1 2 5 1 1 1 1 1 4 1 1 1 2 1 1
## 8000 8050 8500 8800 9000 9550 10000 10500 11200 11600 11730 12000 12200 12748 13740 15000 15100 15500 17890 19000 19245 20000 31330 35000 39000 40000 45000 2e+05
## 1 1 1 1 1 1 5 2 1 1 1 1 1 1 1 5 1 1 1 1 1 1 1 2 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q48. Q775: In the last 30 days how much did the household spend on Medical expenses,
## -999 -998 0 5 6 7 10 12 14 15 18 20 24
## 1 2 1438 2 2 1 6 1 1 1 1 17 1
## 26 30 32 35 40 45 46 48 50 52 55 60 64
## 1 5 2 6 11 2 2 1 25 1 2 7 1
## 65 66 67 68 70 72 73 75 76 78 80 82 83
## 2 1 1 1 4 1 1 2 1 1 3 1 1
## 88 90 93 100 102 105 106 108 110 111 115 119 120
## 1 5 1 26 1 1 2 1 3 1 2 1 6
## 126 129 130 135 136 140 142 150 154 156 160 162 163
## 1 2 5 1 1 2 1 13 1 1 3 1 1
## 170 176 180 185 190 194 195 196 200 210 215 220 224
## 1 1 5 1 1 1 1 1 30 2 1 3 1
## 231 235 236 240 242 250 251 260 266 270 275 280 289
## 1 1 1 4 1 8 1 2 1 1 1 1 1
## 290 299 300 307 310 320 340 350 354 365 367 368 378
## 1 1 19 1 1 5 1 9 1 1 2 1 1
## 390 400 415 420 449 450 456 460 470 474 480 500 506
## 2 17 1 2 1 6 1 1 2 1 1 34 1
## 510 512 516 520 540 550 560 562 575 580 600 608 630
## 2 1 1 1 1 5 2 1 1 1 6 1 1
## 635 640 650 660 666 676 677 683 686 690 696 698 700
## 1 1 4 1 1 1 1 1 1 1 1 1 12
## 710 711 744 750 755 760 762 767 790 792 800 830 840
## 1 1 1 4 1 1 1 1 1 1 12 2 1
## 850 862 870 878 880 896 900 920 930 950 960 970 980
## 2 1 2 1 1 1 5 2 1 4 1 1 1
## 1000 1035 1050 1064 1100 1150 1200 1240 1250 1252 1260 1273 1300
## 40 1 2 1 2 2 16 1 2 1 1 1 5
## 1310 1320 1329 1342 1350 1365 1368 1400 1430 1495 1500 1560 1566
## 1 1 1 1 3 1 1 5 1 1 26 1 1
## 1575 1600 1620 1633 1640 1650 1660 1700 1725 1740 1750 1800 1820
## 1 2 1 1 1 1 1 2 1 1 1 8 1
## 1895 1900 1917 1950 2000 2016 2050 2064 2070 2100 2120 2200 2240
## 1 1 1 2 21 1 1 1 1 2 1 2 1
## 2250 2270 2300 2342 2350 2400 2430 2500 2532 2648 2650 2678 2700
## 1 1 1 1 1 3 1 12 2 1 1 1 1
## 2726 2750 2787 2800 2805 2880 2900 3000 3050 3060 3100 3165 3180
## 1 1 1 2 1 1 2 15 1 1 1 1 1
## 3200 3292 3392 3400 3450 3500 3519 3578 3600 3645 3780 3900 4000
## 3 1 1 1 1 2 1 1 2 1 1 2 10
## 4100 4150 4200 4310 4396 4500 4600 4680 4750 5000 5140 5430 5500
## 2 1 1 1 1 6 2 1 1 13 1 1 2
## 5520 5800 6000 6300 6313 6500 6620 6680 7000 7300 7330 7406 7500
## 1 2 5 1 1 1 1 1 4 1 1 1 2
## 7900 7973 8000 8050 8500 8800 9000 9550 10000 10500 11200 11600 11730
## 1 1 1 1 1 1 1 1 5 2 1 1 1
## 12000 12200 12748 13740 15000 15100 15326 or more
## 1 1 1 1 5 1 12
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q49)[na.exclude(mydata$eh_s10q49)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q49", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q49. Q777: In the last 30 days how much did the household spend on Household durables
## -999 0 8 13 20 25 29 30 35 40 45 48 49 50 51 58 59 60 65 70 72 75 77 80 89 90 95 100 105 110 114 120 130 135 140 145 150 160
## 1 2004 1 1 3 2 1 3 2 8 9 1 1 4 1 1 1 8 1 7 2 2 1 2 1 5 1 17 2 2 1 8 2 1 1 1 17 2
## 170 180 189 200 205 210 229 230 238 240 250 265 275 290 300 320 330 336 350 360 363 370 399 400 408 410 430 450 474 485 490 500 540 550 570 600 630 640
## 2 3 1 14 1 1 1 1 1 5 6 1 1 1 13 2 1 1 2 3 1 2 1 1 1 1 2 1 1 1 1 11 1 1 2 7 1 1
## 650 700 750 799 800 820 850 900 925 940 950 980 1000 1050 1058 1180 1200 1300 1400 1500 1700 2000 2500 2600 2730 3000 3200 3300 3500 3560 4000 5000 <NA>
## 1 7 1 1 1 1 1 2 1 1 1 1 7 1 1 1 2 1 2 7 1 3 1 1 1 2 1 1 3 1 3 1 2
## [1] "Frequency table after encoding"
## eh_s10q49. Q777: In the last 30 days how much did the household spend on Household durables
## -999 0 8 13 20 25 29 30 35 40 45 48 49 50
## 1 2004 1 1 3 2 1 3 2 8 9 1 1 4
## 51 58 59 60 65 70 72 75 77 80 89 90 95 100
## 1 1 1 8 1 7 2 2 1 2 1 5 1 17
## 105 110 114 120 130 135 140 145 150 160 170 180 189 200
## 2 2 1 8 2 1 1 1 17 2 2 3 1 14
## 205 210 229 230 238 240 250 265 275 290 300 320 330 336
## 1 1 1 1 1 5 6 1 1 1 13 2 1 1
## 350 360 363 370 399 400 408 410 430 450 474 485 490 500
## 2 3 1 2 1 1 1 1 2 1 1 1 1 11
## 540 550 570 600 630 640 650 700 750 799 800 820 850 900
## 1 1 2 7 1 1 1 7 1 1 1 1 1 2
## 925 940 950 980 1000 1050 1058 1180 1200 1300 1400 1500 1700 2000
## 1 1 1 1 7 1 1 1 2 1 2 7 1 3
## 2500 2600 2730 2885 or more <NA>
## 1 1 1 12 2
mydata <- top_recode (variable="eh_s10q50", break_point=20, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q50. Q779: In the last 30 days how much did the household spend on Dowry ? Sa nakali
## -999 0 9 20 500 <NA>
## 1 2282 1 1 1 2
## [1] "Frequency table after encoding"
## eh_s10q50. Q779: In the last 30 days how much did the household spend on Dowry ? Sa nakali
## -999 0 9 20 or more <NA>
## 1 2282 1 2 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q51)[na.exclude(mydata$eh_s10q51)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q51", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q51. Q781: In the last 30 days how much did the household spend on Fees paid to baran
## -999 -998 0 8 9 11 15 16 17 20 22 25 28 30 31 35 37 40 42 45 47 50 51 52 53 54 55 56 60 65 67 70 71 75 77 80 82 85
## 1 3 2019 1 1 1 5 1 1 10 1 9 1 12 1 15 1 10 1 4 1 43 1 1 1 1 2 1 7 5 2 4 1 5 1 7 1 1
## 95 100 105 110 115 120 125 130 135 140 150 166 180 190 200 210 250 260 265 285 300 350 400 500 575 850 1380 2100 5550 <NA>
## 1 20 2 4 1 8 1 3 2 1 7 1 3 2 12 1 7 1 2 1 5 2 3 6 1 3 1 1 1 2
## [1] "Frequency table after encoding"
## eh_s10q51. Q781: In the last 30 days how much did the household spend on Fees paid to baran
## -999 -998 0 8 9 11 15 16 17 20 22 25 28 30 31 35
## 1 3 2019 1 1 1 5 1 1 10 1 9 1 12 1 15
## 37 40 42 45 47 50 51 52 53 54 55 56 60 65 67 70
## 1 10 1 4 1 43 1 1 1 1 2 1 7 5 2 4
## 71 75 77 80 82 85 95 100 105 110 115 120 125 130 135 140
## 1 5 1 7 1 1 1 20 2 4 1 8 1 3 2 1
## 150 166 180 190 200 210 250 260 265 285 300 350 400 500 or more <NA>
## 7 1 3 2 12 1 7 1 2 1 5 2 3 13 2
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q54)[na.exclude(mydata$eh_s10q54)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q54", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q54. Q784: How much did you spend on these other expenses in total in the last 12 mon
## -998 0 300 600 800 830 1000 1030 1050 1080 1085 1100 1150 1200 1250 1280 1300 1400 1500 1560 1600 1630 1700 1800 1900 2000 2100 2200 2300 2350 2400 2500
## 3 21 1 1 1 1 16 1 1 1 1 1 1 14 2 1 5 2 31 2 1 1 1 9 1 25 1 1 2 1 2 9
## 2700 2800 2900 3000 3050 3200 3400 3500 3679 4000 4280 4500 5000 5160 5200 5550 6000 6100 6150 6200 6250 6475 6600 6612 7000 7900 8000 8100 10000 10400 10600 10800
## 1 1 3 26 1 4 1 7 1 10 1 3 23 1 1 1 8 1 1 1 1 1 1 1 6 1 4 1 11 1 1 1
## 11000 12000 13000 13200 15000 15315 17500 18480 18500 20000 23050 23250 23400 25000 26400 27000 27600 28000 28800 28805 31640 34000 40000 44400 46000 48000 50000 56000 60000 70000 80000 1e+05
## 3 2 1 1 6 1 1 1 1 8 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1
## 2e+05 3e+05 <NA>
## 1 1 1948
## [1] "Frequency table after encoding"
## eh_s10q54. Q784: How much did you spend on these other expenses in total in the last 12 mon
## -998 0 300 600 800 830 1000 1030 1050 1080 1085 1100
## 3 21 1 1 1 1 16 1 1 1 1 1
## 1150 1200 1250 1280 1300 1400 1500 1560 1600 1630 1700 1800
## 1 14 2 1 5 2 31 2 1 1 1 9
## 1900 2000 2100 2200 2300 2350 2400 2500 2700 2800 2900 3000
## 1 25 1 1 2 1 2 9 1 1 3 26
## 3050 3200 3400 3500 3679 4000 4280 4500 5000 5160 5200 5550
## 1 4 1 7 1 10 1 3 23 1 1 1
## 6000 6100 6150 6200 6250 6475 6600 6612 7000 7900 8000 8100
## 8 1 1 1 1 1 1 1 6 1 4 1
## 10000 10400 10600 10800 11000 12000 13000 13200 15000 15315 17500 18480
## 11 1 1 1 3 2 1 1 6 1 1 1
## 18500 20000 23050 23250 23400 25000 26400 27000 27600 28000 28800 28805
## 1 8 1 1 1 2 1 1 2 1 1 1
## 31640 34000 40000 44400 46000 48000 50000 56000 60000 70000 80000 1e+05
## 1 1 1 1 1 1 3 1 1 1 1 1
## 130500 or more <NA>
## 2 1948
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q55)[na.exclude(mydata$eh_s10q55)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q55", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q55. Q786: Clothing for you? Damit para sa iyo?
## -999 0 2 3 8 20 30 40 50 60 75 80 86 88 90 100 110 120 150 155 160 170 175 180 200 240 250 280 289 300 320 325 350 360 380 399 400 430
## 1 2035 1 1 2 5 1 3 9 2 2 4 1 1 4 39 1 7 14 1 2 1 1 6 22 1 12 3 1 23 1 1 8 1 1 1 9 1
## 450 460 500 525 535 540 560 600 680 700 750 800 1000 2000 3900 4000
## 2 1 25 1 1 1 1 7 1 4 1 1 9 2 1 1
## [1] "Frequency table after encoding"
## eh_s10q55. Q786: Clothing for you? Damit para sa iyo?
## -999 0 2 3 8 20 30 40 50 60 75 80 86 88
## 1 2035 1 1 2 5 1 3 9 2 2 4 1 1
## 90 100 110 120 150 155 160 170 175 180 200 240 250 280
## 4 39 1 7 14 1 2 1 1 6 22 1 12 3
## 289 300 320 325 350 360 380 399 400 430 450 460 500 525
## 1 23 1 1 8 1 1 1 9 1 2 1 25 1
## 535 540 560 600 680 700 750 800 1000 or more
## 1 1 1 7 1 4 1 1 13
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q56)[na.exclude(mydata$eh_s10q56)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q56", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q56. Q787: Clothing for your spouse/partner? Damit para sa asawa/kinakasama mo?
## -999 0 8 10 40 50 60 65 70 75 80 100 104 120 150 160 180 200 210 240 250 260 270 280 300 330 350 360 365 370 400 450 465 500 550 600 650 680
## 1 2149 1 1 1 7 2 1 1 2 2 17 1 4 10 2 2 11 1 1 14 1 1 1 15 1 4 1 1 1 2 2 1 7 1 1 1 1
## 700 750 800 900 1000 1800 5000
## 1 2 1 1 7 1 2
## [1] "Frequency table after encoding"
## eh_s10q56. Q787: Clothing for your spouse/partner? Damit para sa asawa/kinakasama mo?
## -999 0 8 10 40 50 60 65 70 75 80 100 104 120 150 160
## 1 2149 1 1 1 7 2 1 1 2 2 17 1 4 10 2
## 180 200 210 240 250 260 270 280 300 330 350 360 365 370 400 450
## 2 11 1 1 14 1 1 1 15 1 4 1 1 1 2 2
## 465 500 550 600 650 680 700 750 778 or more
## 1 7 1 1 1 1 1 2 12
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q57)[na.exclude(mydata$eh_s10q57)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q57", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q57. Q788: Clothing for the children? Damit para sa mga bata?
## -999 -998 0 1 2 3 10 20 30 40 50 60 70 75 78 80 88 90 95 100 104 105 108 118 120 130 135 140 150 160 170 175
## 1 1 1544 2 3 1 1 1 2 1 9 5 3 1 1 5 1 1 1 36 1 1 1 1 8 4 1 1 20 2 2 1
## 180 185 199 200 210 215 220 225 230 240 250 253 260 270 280 300 308 315 320 330 340 350 358 360 365 370 375 380 390 400 420 430
## 8 1 1 45 1 1 1 1 1 2 29 1 1 1 6 49 1 1 1 3 2 24 1 2 1 3 2 2 1 21 3 2
## 445 450 460 480 499 500 510 520 530 540 550 560 580 590 600 618 630 650 670 700 708 730 737 740 750 760 780 800 810 821 850 880
## 1 8 1 2 1 88 2 1 2 1 9 1 1 1 28 1 1 5 2 14 1 1 1 1 4 2 1 18 2 1 4 1
## 900 926 940 950 975 980 1000 1040 1050 1070 1080 1100 1130 1148 1160 1200 1205 1230 1250 1300 1354 1400 1450 1500 1600 1800 1805 2000 2050 2300 2500 2550
## 6 1 1 1 2 1 68 1 1 1 1 1 1 1 1 7 1 1 1 6 1 2 2 30 1 1 1 20 1 1 7 1
## 2600 2680 2700 3000 3080 3300 3500 3900 4000 4300 5000 6700 7000 8003 1e+05
## 1 1 1 11 1 1 2 1 1 1 2 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q57. Q788: Clothing for the children? Damit para sa mga bata?
## -999 -998 0 1 2 3 10 20 30 40 50 60 70 75
## 1 1 1544 2 3 1 1 1 2 1 9 5 3 1
## 78 80 88 90 95 100 104 105 108 118 120 130 135 140
## 1 5 1 1 1 36 1 1 1 1 8 4 1 1
## 150 160 170 175 180 185 199 200 210 215 220 225 230 240
## 20 2 2 1 8 1 1 45 1 1 1 1 1 2
## 250 253 260 270 280 300 308 315 320 330 340 350 358 360
## 29 1 1 1 6 49 1 1 1 3 2 24 1 2
## 365 370 375 380 390 400 420 430 445 450 460 480 499 500
## 1 3 2 2 1 21 3 2 1 8 1 2 1 88
## 510 520 530 540 550 560 580 590 600 618 630 650 670 700
## 2 1 2 1 9 1 1 1 28 1 1 5 2 14
## 708 730 737 740 750 760 780 800 810 821 850 880 900 926
## 1 1 1 1 4 2 1 18 2 1 4 1 6 1
## 940 950 975 980 1000 1040 1050 1070 1080 1100 1130 1148 1160 1200
## 1 1 2 1 68 1 1 1 1 1 1 1 1 7
## 1205 1230 1250 1300 1354 1400 1450 1500 1600 1800 1805 2000 2050 2300
## 1 1 1 6 1 2 2 30 1 1 1 20 1 1
## 2500 2550 2600 2680 2700 3000 3080 3204 or more
## 7 1 1 1 1 11 1 12
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q58)[na.exclude(mydata$eh_s10q58)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q58", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q58. Q789: Medical expenses for you? Gastos pang medikal para sa iyo?
## 0 1 2 7 8 10 13 15 16 18 20 21 24 25 27 28 30 35 40 45 46 48 50 56 60 65 66 68 70 72 75 78
## 1894 3 1 1 1 2 2 2 1 3 12 2 1 1 1 1 11 1 7 1 1 4 27 1 5 1 2 2 3 1 1 1
## 80 82 86 90 93 100 106 108 120 130 147 150 154 157 160 188 190 200 220 241 250 275 280 290 300 314 320 350 360 363 368 400
## 2 1 1 1 1 38 1 1 8 2 1 5 1 1 1 1 1 21 1 1 2 1 2 1 14 1 1 3 1 1 1 1
## 430 449 450 500 520 550 560 600 635 650 700 710 730 770 800 870 900 930 950 960 1000 1035 1200 1252 1295 1300 1373 1400 1450 1491 1500 1600
## 1 1 2 21 1 1 1 7 1 1 6 1 1 1 2 1 4 1 2 1 23 1 8 1 1 3 1 1 1 1 8 1
## 1620 1633 1700 1800 1900 2000 2016 2400 2500 2650 2700 2800 3000 3500 3519 4000 4600 5000 6000 6500 7000 7300 8000 8800 9000 10000 12000 12748 15000 20000 35000 45000
## 1 1 3 4 1 9 1 2 2 1 1 1 10 1 1 4 1 4 2 1 1 1 1 1 1 1 1 1 2 1 1 1
## 2e+05
## 1
## [1] "Frequency table after encoding"
## eh_s10q58. Q789: Medical expenses for you? Gastos pang medikal para sa iyo?
## 0 1 2 7 8 10 13 15 16 18 20 21 24 25
## 1894 3 1 1 1 2 2 2 1 3 12 2 1 1
## 27 28 30 35 40 45 46 48 50 56 60 65 66 68
## 1 1 11 1 7 1 1 4 27 1 5 1 2 2
## 70 72 75 78 80 82 86 90 93 100 106 108 120 130
## 3 1 1 1 2 1 1 1 1 38 1 1 8 2
## 147 150 154 157 160 188 190 200 220 241 250 275 280 290
## 1 5 1 1 1 1 1 21 1 1 2 1 2 1
## 300 314 320 350 360 363 368 400 430 449 450 500 520 550
## 14 1 1 3 1 1 1 1 1 1 2 21 1 1
## 560 600 635 650 700 710 730 770 800 870 900 930 950 960
## 1 7 1 1 6 1 1 1 2 1 4 1 2 1
## 1000 1035 1200 1252 1295 1300 1373 1400 1450 1491 1500 1600 1620 1633
## 23 1 8 1 1 3 1 1 1 1 8 1 1 1
## 1700 1800 1900 2000 2016 2400 2500 2650 2700 2800 3000 3500 3519 4000
## 3 4 1 9 1 2 2 1 1 1 10 1 1 4
## 4600 5000 6000 6500 7000 7300 7695 or more
## 1 4 2 1 1 1 12
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q59)[na.exclude(mydata$eh_s10q59)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q59", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q59. Q790: Medical expenses for your spouse/partner? Gastos pang medikal para sa asa
## 0 1 4 6 7 8 10 20 21 22 25 27 28 29 30 32 36 39 40 45 50 51 52 58 60 62 63 66 67 70 72 75
## 1970 2 1 1 1 1 3 7 1 2 2 1 1 1 7 1 1 1 5 1 19 2 1 1 6 1 2 1 1 1 1 2
## 76 79 90 95 100 106 108 115 120 126 150 180 195 200 210 224 230 245 250 280 300 350 371 380 400 420 448 450 480 500 520 550
## 1 1 2 1 25 1 1 1 2 1 12 2 1 19 1 1 3 1 4 1 20 2 1 1 5 1 1 2 1 19 1 1
## 600 700 750 767 800 896 992 1000 1010 1050 1064 1200 1250 1270 1300 1368 1400 1500 1700 1900 2000 2010 2060 2456 2600 2750 2868 3000 3450 3500 3800 4000
## 4 1 3 1 2 1 1 13 1 1 1 2 1 1 1 1 2 9 1 1 8 1 1 1 1 1 1 6 1 2 1 3
## 4080 4500 5000 5140 6000 7000 7300 8000 10000 12000 13600 15000 35000 39000
## 1 2 9 1 5 1 1 1 3 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q59. Q790: Medical expenses for your spouse/partner? Gastos pang medikal para sa asa
## 0 1 4 6 7 8 10 20 21 22 25 27 28 29
## 1970 2 1 1 1 1 3 7 1 2 2 1 1 1
## 30 32 36 39 40 45 50 51 52 58 60 62 63 66
## 7 1 1 1 5 1 19 2 1 1 6 1 2 1
## 67 70 72 75 76 79 90 95 100 106 108 115 120 126
## 1 1 1 2 1 1 2 1 25 1 1 1 2 1
## 150 180 195 200 210 224 230 245 250 280 300 350 371 380
## 12 2 1 19 1 1 3 1 4 1 20 2 1 1
## 400 420 448 450 480 500 520 550 600 700 750 767 800 896
## 5 1 1 2 1 19 1 1 4 1 3 1 2 1
## 992 1000 1010 1050 1064 1200 1250 1270 1300 1368 1400 1500 1700 1900
## 1 13 1 1 1 2 1 1 1 1 2 9 1 1
## 2000 2010 2060 2456 2600 2750 2868 3000 3450 3500 3800 4000 4080 4500
## 8 1 1 1 1 1 1 6 1 2 1 3 1 2
## 5000 5140 6000 or more
## 9 1 16
percentile_99.5 <- floor(quantile(na.exclude(mydata$eh_s10q60)[na.exclude(mydata$eh_s10q60)!=999999], probs = c(0.995)))
mydata <- top_recode (variable="eh_s10q60", break_point=percentile_99.5, missing=999999)## [1] "Frequency table before encoding"
## eh_s10q60. Q791: Medical expenses and vaccinations for the children of the household? Gast
## 0 1 5 6 7 8 10 12 14 15 18 20 21 22 23 24 25 30 35 40 42 45 46 50 52 55 56 57 60 65 70 73
## 1773 2 3 2 1 1 7 1 2 1 1 11 1 2 1 1 1 14 6 4 1 2 2 14 1 1 2 1 5 3 3 1
## 75 76 78 80 83 85 90 95 100 102 104 105 110 115 118 119 120 126 130 135 140 145 150 158 160 162 165 166 174 176 180 183
## 1 1 1 4 1 2 5 1 14 1 1 1 2 1 1 1 8 1 1 1 1 2 10 1 1 1 1 1 1 2 5 1
## 185 190 196 200 203 205 206 210 215 220 224 231 235 240 242 246 250 251 260 270 289 295 296 300 310 315 320 322 340 350 360 365
## 1 1 1 15 1 1 1 1 1 1 1 1 1 1 1 1 8 1 1 1 1 1 2 17 1 1 1 1 1 8 2 1
## 370 374 378 380 390 400 436 450 456 470 473 500 506 530 540 545 560 562 566 570 584 600 640 650 656 662 673 683 686 700 710 711
## 1 1 1 1 1 10 1 3 1 1 1 17 1 1 1 1 1 1 1 2 1 8 1 7 1 1 1 1 1 7 1 1
## 750 780 800 850 900 920 935 950 980 1000 1010 1020 1040 1041 1063 1160 1170 1200 1240 1250 1275 1300 1350 1365 1390 1400 1425 1500 1520 1560 1575 1600
## 3 1 9 2 3 1 1 4 1 24 1 1 1 1 1 1 1 5 1 1 1 4 1 1 2 3 1 8 1 1 1 2
## 1620 1640 1700 1740 1750 1800 1917 2000 2042 2100 2101 2200 2240 2300 2350 2400 2500 2550 2600 2603 2650 2696 2700 2718 2787 2830 2900 3000 3050 3100 3165 3200
## 1 1 1 1 1 3 1 11 1 2 1 1 1 2 1 1 9 1 1 1 1 1 1 1 1 1 1 6 1 1 1 2
## 3300 3500 3645 3960 4000 4100 4500 4740 5000 5300 5430 5506 6000 7000 7890 10000 11600 13650 15000 15500 19245 20000 40000
## 2 1 1 1 4 1 1 1 6 1 1 1 2 2 1 2 1 1 1 1 1 1 1
## [1] "Frequency table after encoding"
## eh_s10q60. Q791: Medical expenses and vaccinations for the children of the household? Gast
## 0 1 5 6 7 8 10 12 14 15 18 20 21 22
## 1773 2 3 2 1 1 7 1 2 1 1 11 1 2
## 23 24 25 30 35 40 42 45 46 50 52 55 56 57
## 1 1 1 14 6 4 1 2 2 14 1 1 2 1
## 60 65 70 73 75 76 78 80 83 85 90 95 100 102
## 5 3 3 1 1 1 1 4 1 2 5 1 14 1
## 104 105 110 115 118 119 120 126 130 135 140 145 150 158
## 1 1 2 1 1 1 8 1 1 1 1 2 10 1
## 160 162 165 166 174 176 180 183 185 190 196 200 203 205
## 1 1 1 1 1 2 5 1 1 1 1 15 1 1
## 206 210 215 220 224 231 235 240 242 246 250 251 260 270
## 1 1 1 1 1 1 1 1 1 1 8 1 1 1
## 289 295 296 300 310 315 320 322 340 350 360 365 370 374
## 1 1 2 17 1 1 1 1 1 8 2 1 1 1
## 378 380 390 400 436 450 456 470 473 500 506 530 540 545
## 1 1 1 10 1 3 1 1 1 17 1 1 1 1
## 560 562 566 570 584 600 640 650 656 662 673 683 686 700
## 1 1 1 2 1 8 1 7 1 1 1 1 1 7
## 710 711 750 780 800 850 900 920 935 950 980 1000 1010 1020
## 1 1 3 1 9 2 3 1 1 4 1 24 1 1
## 1040 1041 1063 1160 1170 1200 1240 1250 1275 1300 1350 1365 1390 1400
## 1 1 1 1 1 5 1 1 1 4 1 1 2 3
## 1425 1500 1520 1560 1575 1600 1620 1640 1700 1740 1750 1800 1917 2000
## 1 8 1 1 1 2 1 1 1 1 1 3 1 11
## 2042 2100 2101 2200 2240 2300 2350 2400 2500 2550 2600 2603 2650 2696
## 1 2 1 1 1 2 1 1 9 1 1 1 1 1
## 2700 2718 2787 2830 2900 3000 3050 3100 3165 3200 3300 3500 3645 3960
## 1 1 1 1 1 6 1 1 1 2 2 1 1 1
## 4000 4100 4500 4740 5000 5300 5430 5506 6000 6565 or more
## 4 1 1 1 6 1 1 1 2 12
Indirect PII - Categorical: Recode, encode, or Top/bottom coding for extreme values
# !!!No Indirect PII - CategoricalMatching and crosstabulations: Run automated PII check
# !!!Insufficient demographic dataOpen-ends: review responses for any sensitive information, redact as necessary
# !!! Identify open-end variables here:
open_ends <- c("eh_s10q29",
"eh_s10q53")
report_open (list_open_ends = open_ends)
# Review "verbatims.csv". Identify variables to be deleted or redacted and their row number
mydata$eh_s10q29[157] <- "[language]"
mydata$eh_s10q29[652] <- "[language]"
mydata$eh_s10q29[755] <- "[language]"
mydata$eh_s10q29[795] <- "[language]"
mydata$eh_s10q29[828] <- "[language]"
mydata$eh_s10q29[829] <- "[language]"
mydata$eh_s10q29[844] <- "[language]"
mydata$eh_s10q29[862] <- "[language]"
mydata$eh_s10q29[879] <- "[language]"
mydata$eh_s10q29[884] <- "[language]"
mydata$eh_s10q29[891] <- "[language]"
mydata$eh_s10q29[920] <- "[language]"
mydata$eh_s10q29[923] <- "[language]"
mydata$eh_s10q29[924] <- "[language]"
mydata$eh_s10q29[927] <- "[language]"
mydata$eh_s10q29[950] <- "[language]"
mydata$eh_s10q29[966] <- "[language]"
mydata$eh_s10q29[972] <- "[language]"
mydata$eh_s10q29[973] <- "[language]"
mydata$eh_s10q29[974] <- "[language]"
mydata$eh_s10q29[978] <- "[language]"
mydata$eh_s10q29[1009] <- "[language]"
mydata$eh_s10q29[1209] <- "[language]"
mydata$eh_s10q29[1228] <- "[language]"
mydata$eh_s10q29[1389] <- "[language]"
mydata$eh_s10q29[1493] <- "[language]"
mydata$eh_s10q29[1582] <- "[language]"
mydata$eh_s10q29[1616] <- "Food for [name]'s [event]"
mydata$eh_s10q29[1628] <- "[language]"
mydata$eh_s10q29[1651] <- "[language]"
mydata$eh_s10q29[1685] <- "[language]"
mydata$eh_s10q29[1735] <- "[language]"
mydata$eh_s10q29[1845] <- "[language]"
mydata$eh_s10q29[1866] <- "[language]"
mydata$eh_s10q29[1876] <- "[language]"
mydata$eh_s10q29[1919] <- "[language]"
mydata$eh_s10q29[1921] <- "[language]"
mydata$eh_s10q29[1945] <- "[language]"
mydata$eh_s10q29[1958] <- "[language]"
mydata$eh_s10q29[1962] <- "[language]"
mydata$eh_s10q29[1979] <- "[language]"
mydata$eh_s10q29[2000] <- "[language]"
mydata$eh_s10q29[2011] <- "[language]"
mydata$eh_s10q29[2050] <- "[language]"
mydata$eh_s10q29[2052] <- "[language]"
mydata$eh_s10q29[2053] <- "[language]"
mydata$eh_s10q29[2074] <- "[language]"
mydata$eh_s10q29[2119] <- "[language]"
mydata$eh_s10q29[2160] <- "[language]"
mydata$eh_s10q29[2169] <- "[language]"
mydata$eh_s10q29[2235] <- "[language]"
mydata$eh_s10q29[2241] <- "[language]"
mydata$eh_s10q29[2246] <- "[language]"
mydata$eh_s10q29[2268] <- "[language]"
mydata$eh_s10q29[2282] <- "[language]"
mydata$eh_s10q53[5] <- "[language]"
mydata$eh_s10q53[17] <- "materials"
mydata$eh_s10q53[19] <- "licensed ([store])"
mydata$eh_s10q53[28] <- "[amount redacted]"
mydata$eh_s10q53[48] <- "Tuition fee of [name]"
mydata$eh_s10q53[76] <- "[amount redacted]"
mydata$eh_s10q53[101] <- "materials "
mydata$eh_s10q53[113] <- "[language]"
mydata$eh_s10q53[130] <- "[repaires]"
mydata$eh_s10q53[144] <- "[repaires]"
mydata$eh_s10q53[164] <- "[event]"
mydata$eh_s10q53[176] <- "[language]"
mydata$eh_s10q53[185] <- "[training] - [name]"
mydata$eh_s10q53[189] <- "[repaires]"
mydata$eh_s10q53[201] <- "[celebration]"
mydata$eh_s10q53[207] <- "[repaires]"
mydata$eh_s10q53[244] <- "[repaires]"
mydata$eh_s10q53[246] <- "[repaires]"
mydata$eh_s10q53[247] <- "[repaires]"
mydata$eh_s10q53[272] <- "Medicine for [person]"
mydata$eh_s10q53[278] <- "Medical expenses of [person]"
mydata$eh_s10q53[307] <- "[language]"
mydata$eh_s10q53[334] <- "[repaires]"
mydata$eh_s10q53[403] <- "Medical expenses for the [persons]"
mydata$eh_s10q53[430] <- "[event]"
mydata$eh_s10q53[450] <- "[amount redacted]"
mydata$eh_s10q53[487] <- "Gown rent for [event] of her son"
mydata$eh_s10q53[511] <- "Medical expenses for the [person]"
mydata$eh_s10q53[544] <- "[language]"
mydata$eh_s10q53[547] <- "[amount redacted]"
mydata$eh_s10q53[549] <- "[work]"
mydata$eh_s10q53[556] <- "[amount redacted]"
mydata$eh_s10q53[566] <- "[other]"
mydata$eh_s10q53[689] <- "[event]"
mydata$eh_s10q53[767] <- "[language]"
mydata$eh_s10q53[794] <- "Business permit and [repaires]"
mydata$eh_s10q53[828] <- "Medical expenses for [person]"
mydata$eh_s10q53[848] <- "[language]"
mydata$eh_s10q53[866] <- "[language]"
mydata$eh_s10q53[889] <- "[language]"
mydata$eh_s10q53[896] <- "[language]"
mydata$eh_s10q53[897] <- "Travel to [place] and [event] of [name and date]"
mydata$eh_s10q53[931] <- "[language]"
mydata$eh_s10q53[932] <- "[language]"
mydata$eh_s10q53[949] <- "Materials "
mydata$eh_s10q53[954] <- "[language]"
mydata$eh_s10q53[967] <- "[language]"
mydata$eh_s10q53[1029] <- "[language]"
mydata$eh_s10q53[1031] <- "[language]"
mydata$eh_s10q53[1046] <- "[language]"
mydata$eh_s10q53[1062] <- "Travel expenses to relatuve in [place]"
mydata$eh_s10q53[1072] <- "[repaires] and school materials"
mydata$eh_s10q53[1077] <- "For applying work in [place](daughter [name])"
mydata$eh_s10q53[1088] <- "[situation]"
mydata$eh_s10q53[1106] <- "[person] hospitalized"
mydata$eh_s10q53[1115] <- "Field trip - [amount redacted]"
mydata$eh_s10q53[1194] <- "School project of [name]"
mydata$eh_s10q53[1196] <- "Medical expenses for [people]"
mydata$eh_s10q53[1264] <- "Fare from ([places])"
mydata$eh_s10q53[1277] <- " (Work Requirements)"
mydata$eh_s10q53[1282] <- "[medical expenses]"
mydata$eh_s10q53[1388] <- "[amount redacted]"
mydata$eh_s10q53[1407] <- "[repaires]"
mydata$eh_s10q53[1462] <- "Medical Expense on [date]"
mydata$eh_s10q53[1512] <- "[amount redacted]"
mydata$eh_s10q53[1519] <- "[amount redacted]"
mydata$eh_s10q53[1540] <- "[language]"
mydata$eh_s10q53[1581] <- "Transportation from [place] going to [place] to [purpose]"
mydata$eh_s10q53[1587] <- "[repaires]"
mydata$eh_s10q53[1627] <- "graduation"
mydata$eh_s10q53[1651] <- "[materials]"
mydata$eh_s10q53[1666] <- "[amount redacted]"
mydata$eh_s10q53[1694] <- "Graduation"
mydata$eh_s10q53[1713] <- "[materials]"
mydata$eh_s10q53[1727] <- "[language]"
mydata$eh_s10q53[1737] <- "Helping with the burial of [person]"
mydata$eh_s10q53[1738] <- "[language]"
mydata$eh_s10q53[1754] <- "[repaires]"
mydata$eh_s10q53[1757] <- "[language]"
mydata$eh_s10q53[1761] <- "[event]"
mydata$eh_s10q53[1785] <- "[repaires]"
mydata$eh_s10q53[1808] <- "Enrolment fee [amount redacted] scholar [amount redacted] per sem"
mydata$eh_s10q53[1811] <- "[repaires]"
mydata$eh_s10q53[1821] <- "[amount redacted]"
mydata$eh_s10q53[1843] <- "[repaires]"
mydata$eh_s10q53[1856] <- "For medication of [person] and graduation "
mydata$eh_s10q53[1897] <- "[illness]"
mydata$eh_s10q53[1908] <- "[amount redacted]"
mydata$eh_s10q53[1916] <- "[amount redacted]"
mydata$eh_s10q53[1919] <- "[language]"
mydata$eh_s10q53[1924] <- "Hospitalization of [name]"
mydata$eh_s10q53[1937] <- "[amount redacted]"
mydata$eh_s10q53[2007] <- "License"
mydata$eh_s10q53[2023] <- "[name]'s birthday"
mydata$eh_s10q53[2074] <- "Birthday and anniversary [date]"
mydata$eh_s10q53[2078] <- "[language]"
mydata$eh_s10q53[2126] <- "[amount redacted]"
mydata$eh_s10q53[2160] <- "[language]"
mydata$eh_s10q53[2181] <- "[language]"
mydata$eh_s10q53[2190] <- "[repaires]"
mydata$eh_s10q53[2200] <- "[language]"
mydata$eh_s10q53[2209] <- "[repaires] and graduation"
mydata$eh_s10q53[2256] <- "[language]"
mydata$eh_s10q53[2280] <- "[language]"
mydata$eh_s10q53[2281] <- "Hospitalization . [date]"GPS data: Displace
# !!!No GPS dataSave processed data in Stata and SPSS format
haven::write_dta(mydata, paste0(filename, "_PU.dta"))
haven::write_sav(mydata, paste0(filename, "_PU.sav"))
# Add report title dynamically
title_var <- paste0("DOL-ILAB SDC - ", filename)